Imagine this: you’re sitting at home watching the news. An employee in a major beverage company is coming out of the courthouse in an ongoing, much publicized legal battle with her employers. All she did was inform government officials of a specific component that has been added to a recent product without proper tests. After going through the proper channels and filing a complaint at the company, she was told that it would be investigated, but nothing happened. 

She took action. Because someone had to.

The company argues that she signed a non-disclosure agreement (NDA) that prevents any employee from divulging trade secrets. Her lawyers counter that when it comes to public health, NDAs are not legally (nor ethically) binding. 

You have been following this case from the start, and you’re waiting for the court-ordered independent lab results to settle the matter. 

Now, ask yourself this: which part of that scenario did you care about most? You’d only be human if the first thing that came to mind was personal interest. When it is a health-related topic, everyone’s first priority is – and should be –  their own. The second thing might be a coin-toss between how the court would penalize the company if wrongdoing was proven, and how government officials would try to revise laws on food additives.

Coming last would be the employee who reported it. Why? Because we all assume she’ll be fine.  She actually did the right thing and her actions will save lives. If the courts rule in her favor, she’ll be lauded as a heroine. She presented evidence: lab results, testimonies, and her own eyewitness account. What more is there to think about?

She’ll be fine.

And you’d be right to think that way. In the United States, whistleblowers in the food industry are protected under the Food and Drug Administration (FDA) Food Safety Modernization Act (FSMA). The company can’t retaliate against her in any way for whistleblowing: by dismissal, demotion, or transfer. Should that happen, she’d be within her legal right to file a complaint, win it, and likely be compensated and reinstated back to her position.

Let’s use the same scenario but shift the industry; this time it’s an AI company. Here, the story takes a different arc. 

The common theme among AI whistleblowers differs from that of the food and beverage industry. In the absence of clear laws and reporting channels, the decision to speak out against AI industry giants is often weighed against livelihood, personal and professional reputation, and, in one case, life itself.

Stories from the AI Trenches

Sounding the alarm takes not just evidence, but courage and a firm moral belief that you’re doing the right thing: telling the truth. 

Meet a few brave individuals who faced hard obstacles to let the world know what goes on behind closed lab doors.

Leopold Aschenbrenner: Former OpenAI safety researcher, fired from his position for allegedly sharing documents externally. He warned of “egregiously insufficient” security against foreign threats, citing OpenAI’s failure to adequately protect critical algorithmic information and model weights. He became an advocate, urging a shift away from fast, unsafe deployment towards robust safety measures.

Suchir Balaji: Former researcher at OpenAI and perhaps the most tragic story on this list. He resigned in August 2024 after stating the company’s use of copyrighted material violated U.S. law and posed commercial harm to creators. He was set to testify in intellectual property lawsuits against OpenAI; but was tragically found dead by suicide on November 26, 2024. 

Timnit Gebru: Co-lead of Google’s Ethical AI team, dismissed in 2020 after writing a research paper that exposed how current AI training methods could deepen biases against minorities and marginalized communities. Her dismissal gained widespread coverage, exposing corporate retaliation, prompting her to found the Distributed AI Research Institute (DAIR).

Louis Hunt: Former CFO and VP of Business Development at Liquid AI. He resigned from his position and publicly challenged the claim that AI models don’t replicate copyrighted works. He  presented evidence of generated outputs that were exact copies of texts from The New York Times, Stephen King’s books, and Harvard Business Publishing articles.

Margaret Mitchell: Founder and co-lead of Google AI ethics unit who was fired in 2021 for alleged misconduct. She testified in the September 2024 Senate hearing on AI oversight, stressing the need for clear instructions to employees navigating NDAs, and accessible whistleblowing channels to provide support to those who wish to come forward with their concerns.

William Saunders: Ex-OpenAI technical staff member who testified at the September 2024 Senate hearing. He advised on: 1) establishing a list of government contacts who understood the reported issues and could act on them, and 2) identifying legal protections insiders need when flagging actions that don’t break laws, but put public safety at risk.

Helen Toner: Former OpenAI board member on the nonprofit arm, who testified at the September 2024 Senate hearing. She highlighted how vagueness in current AI whistleblower laws discourages people from coming forward, specifically those with complaints about AI development that often don’t fit existing legal categories designed for traditional industries.

Anonymous and Named Whistleblowers: Group of eleven-thirteen current and former employees at leading AI companies who in June 2024 wrote an open letter “Right to Warn” calling for principles to create a safe environment for employees to voice their concerns on potential risks. In July 2024, they filed a complaint to the Securities and Exchange Commission (SEC) requesting an investigation into how NDAs restrict scrutiny of safety behaviors (e.g., the rushed testing of GPT-4o). 

This list is not exhaustive, many more names can be mentioned, including, David Evan Harris, Jacob Hilton, Geoffrey Hinton, Daniel Kokotajlo, Ramana Kumar, Jan Leike, Neel Nanda, Carroll Wainwright, and Daniel Ziegler, among others. 

Why Blow the Whistle

Merriam-Webster defines a whistleblower as “an employee who brings wrongdoing by an employer or by other employees to the attention of a government or law enforcement agency,” followed by the following note: “A whistleblower is commonly protected legally from retaliation.” 

If we analyze the stories above based on that definition, we see events unfolding in two stages. Stage One: the employee notices wrongdoing and first reports it internally. When the response from the company dismisses the complaint or addresses it inadequately, the employee either reports their concerns to the government/the media or publishes their findings as independent research.

The complaints themselves span a wide range, from ethical and governance concerns to more technical issues of alignment and safety. In addition, the alarms raised aren’t about one company’s specific attitude towards AI training, testing, and deployment; they bring to light an industry-wide, systemic pattern of behavior that if left unchecked could lead to non-reversable consequences.

For an insider to have that first-hand experience and courage to go through the steps to prevent such dangers, their actions should be celebrated as a reflection of moral integrity, not cause for retaliation. This brings us to the second part of the definition where the key term is “commonly protected”. Commonly means usually/often and that is an apt description in the case of AI whistleblowers. 

Stage Two is where we fail them, because there is simply no protection. The stories show that all of them lost their jobs, either by dismissal (with or without stated reasons) or through resignation (as a form of protest). They became industry pariahs just for speaking up, and some couldn’t find employment immediately after their stories broke out. 

If you’re an employee in an AI frontier company and read about the consequences faced by those who actually wanted to help, would you risk your career/livelihood, your industry standing, and your future to report AI risks? 

Nuclear Whistleblowing as a Model

In recent years, AI research has seen a surge in academic papers, books, interviews, and podcasts that wrestle with growing safety concerns. These works highlight AI’s dual-use and emphasize its black box nature – the opaque decision-making processes that continue to baffle even the scientists building these systems. 

In response, many AI governance and ethics experts compare the risks posed by advanced AI systems to those of the Chemical, Biological, Radiological, and Nuclear (CBRN) materials and agents in their severity and potential for catastrophic and even existential harm.

Therefore, as a model for the AI industry, I turn to the nuclear field. Its whistleblower laws and regulatory frameworks have long maintained vigilant oversight over both plant operations and worker safety.

The Energy Reorganization Act of 1974 (ERA) established the Nuclear Regulatory Commission (NRC), which regulates civilian (not military) nuclear facilities and materials. The Act was substantially strengthened in 1992 through the Comprehensive National Energy Policy Act, which added significant whistleblower protections including Section 211.

The key provisions in this amendment include the following:

1. It defines the protected activities for which employees cannot face discrimination. 
2. It establishes a clear complaint and investigation process for whistleblowers who face retaliation. 
3. It requires that these protections be posted permanently in the workplace for constant employee access
4. It directs the NRC or the Department of Energy (DOE) to conduct swift investigations into whistleblower allegations of substantial safety hazards.

Here are examples of how this works in practice:

In May 2013, the Occupational Safety and Health Administration (OSHA) found that Enercon Services Inc. wrongfully fired a senior engineer at Wolf Creek Generating Station for reporting safety violations. The engineer’s employment had been terminated in January 2012 after pointing out that soil coverage for buried safety pipes didn’t meet federal requirements. He was also asked to write a report justifying inadequate backfill material, but he refused.

OSHA ordered the company to reinstate the engineer with back pay, benefits, and compensatory damages. Enercon appealed, claiming the termination was for legitimate business reasons, but investigators found the engineer’s concerns were valid and that the field errors weren’t his fault.

In 2016, the Government Accountability Office (GAO) released a comprehensive review of DOE whistleblower protections and found that while the legal framework exists, enforcement and implementation needed strengthening. The report’s six recommendations, which were mostly accepted, demonstrate that Nuclear whistleblower protections are actively monitored, regularly evaluated, and continuously improved to address gaps, and ensure the law’s promise is realized.

In 2024, the U.S. Supreme Court ruled in Murray v. UBS Securities that whistleblowers under the Sarbanes-Oxley Act (SOX) don’t need to prove their employer intended to retaliate. The Second Circuit Court of Appeals had required plaintiffs to prove employer’s intent, but the Supreme Court vacated that decision. It held that if an employer treats someone worse (by firing, demoting, or changing their working conditions) because of whistleblowing (a protected activity), that constitutes a violation. The employer’s motivation is irrelevant.

This ruling is significant because the Supreme Court noted that the Energy Reorganization Act protections already worked this way. Under ERA, whistleblowers only need to show their protected activity was a factor in the adverse action. Then the employer must prove by clear and convincing evidence they would have taken the same action anyway. The Murray decision reinforced what nuclear whistleblowers already have: strong legal protections that shift the burden away from those who speak out.

The established path for Nuclear whistleblowers could serve as a blueprint for their AI colleagues, who operate in a similarly high-risk domain.

The Push for AI Whistleblower Protection: A Timeline

We now have a question: if AI risks are widely acknowledged as severe, from systemic bias to existential threat, why does the law still fail to protect those who report these risks? The answer is quite simple: existing legal frameworks typically require evidence of fraud or illegality – thresholds that may not encompass AI safety concerns. This leaves insiders vulnerable as they attempt to alert the public and policymakers. 

The following timeline traces key events from 2024 to 2025 that have shaped the need for whistleblower protection. 

In May 2024, news of OpenAI’s restrictive NDAs became known. Following public criticism, CEO Sam Altman admitted he was unaware of their extent and expressed embarrassment. He confirmed the company’s revisions of these agreements to remove provisions that threatened to deprive departing employees of their vested equity. This acknowledgment came after reports that OpenAI’s NDAs prohibited ex-employees from criticizing the company or disclosing safety concerns, sparking broader scrutiny of AI industry practices.

In June, thirteen current and former AI employees wrote an open letter “Right to Warn” urging frontier AI companies to promote an environment of safety-first in AI development and deployment. They stressed the current practice of forcing new hires to sign an NDA, the terms of which demand that they cannot voice concerns or disparage the company, even after leaving.

Moreover, they raised the point of widespread retaliation against whistleblowers, which adds to the growing concern that AI companies fear losing funding and investments more than protecting humanity from a technology that could possibly destroy it. By not allowing criticism, the companies silence well-meaning experts who could steer innovation in the right direction.

On July 1, the same group filed a formal complaint to SEC’s chairman Gary Gensler and to Senator Chuck Grassley’s office. In it, they provided evidence that OpenAI’s NDAs were restrictive to any protected disclosures of concerns related to AI safety, and asked the chairman to conduct an investigation into whether that practice broke SEC rules.

On July 31, OpenAI sent a letter to senators outlining robust safety measures, including dedicating 20% of computing resources to safety efforts like red-teaming and risk evaluations. The letter also affirmed support for whistleblower protections by introducing anonymous reporting options, for example: the Integrity Line.

On August 1, Sen. Grassley sent a letter to Sam Altman demanding answers about OpenAI’s restrictive NDAs with a deadline of August 15. The questions included:

1. Whether OpenAI changed the restrictive language of their NDAs, and provide proof of it.
2. The number of employees who requested to contact federal authorities, including all the relevant details.
3. The number of SEC investigations into OpenAI, including basis and outcome.

Sen. Grassley’s aim was to identify the purpose of the NDAs: whether it is for protecting trade secrets, or preventing employees from voicing their concerns.

On September 17, Sen. Blumenthal chaired a hearing by the Senate Committee on the Judiciary’s Subcommittee on Privacy, Technology, and the Law. He and several senators heard testimonies from, and directed questions to, expert witnesses on current AI regulations from an insider’s perspective. The hearing covered topics relevant to AI safety implementation and whistleblower protection.

At the start of 2025, President Trump signed Executive Order 14179 in January titled “Removing Barriers to American Leadership in Artificial Intelligence,” which revoked Biden’s AI safety order (EO 14110 of 2023). The new EO mandated the creation of an AI action plan within 180 days, and explicitly set the policy of maintaining U.S. “AI dominance” by removing regulatory barriers to innovation. This shift in policy marked a decisive turn towards deregulation, one that positioned acceleration as a national priority.

In May, Sen. Grassley and bipartisan cosponsors introduced the “AI Whistleblower Protection Act” (S. 1792) in response to mounting concerns about retaliation against AI employees who raise safety issues. The bill would prohibit retaliation against both employees and independent contractors who report AI security vulnerabilities or safety violations. As of October 2025, the bill has yet to advance beyond the Committee on Health, Education, Labor, and Pensions.

Following the executive order mandate, the White House released the AI Action Plan in July. It advances the deregulatory vision by explicitly prioritizing speed and innovation, while dismantling what it calls “onerous regulation” and “bureaucratic red tape.” By eliminating safety requirements, the plan effectively grants the private sector carte blanche to accelerate AI deployment with minimal oversight.

In October, California passed SB-53 “Transparency in Frontier AI Act”, the first U.S. state law establishing frontier AI transparency and whistleblower protections. Among other provisions, SB-53 introduces direct whistleblower protections for covered employees tasked with assessing or managing critical safety risks:

• It mandates that large AI developers establish anonymous reporting channels.
• Employees are shielded from retaliation when using these channels or when reporting externally to state or federal authorities.
• Protection applies when the whistleblower reasonably believes their employer’s actions pose a substantial threat to public health or safety; especially in cases involving catastrophic risk.

These provisions mark a shift from viewing whistleblowers as disruptors to recognizing them as vital protectors of public interest.

From corporate controversies to whistleblower advocacy and legislative breakthroughs, the road to stronger safeguards has been anything but easy, especially when the drive for rapid deployment outweighs the call for scrutiny. Yet through the combined efforts of courageous individuals and responsive institutions, robust protections are finally within reach.

Line of Defense

In her testimony at the September 17 2024 Senate hearing, Dr Mitchell compared AI training to baking:

“With Data as essentially ingredients, training is cooking, and the model is the output … we’re missing an approach where we have recipes, a deep understanding of what the pieces are that result in this output.”

In a landscape where the builders themselves don’t fully grasp the systems they create, insider disclosures may be the only path to effective regulation.

As the race to develop and deploy AI accelerates, safety and alignment concerns take a back seat at frontier companies. Governance frameworks are racing to catch up by creating laws and policies to protect public welfare, but the gap between innovation and governance remains wide. We need whistleblowers now more than ever. 

But how can we rely on insiders to shield us from high-stakes AI failures when they lack industry-wide protections and remain vulnerable to retaliation? Whistleblowers are our primary line of defense in this field. But shields need protection too. Without enforceable legal guarantees, we’re asking people to make sacrifices they shouldn’t have to.

Where is the justice in that?

The post Whistleblowing as a Shield: Protecting the Voices That Keep AI Safe  appeared first on Untold.

But as AI becomes more embedded in governance and security, its role in border enforcement and immigration control has grown rapidly. These technologies often reproduce and intensify racial discrimination, particularly through algorithmic bias. This is no less relevant in the U.S. government’s utilization of the so-called “smart border”. 

What happens when AI is deployed to decide who can move, who is detained, and who is excluded at the border? 

A Human Rights Framework

In response to a 2023 meeting with United Nations Special Rapporteur on contemporary forms of racism, racial discrimination, xenophobia and related intolerance, the Black Alliance for Just Immigration (BAJI) and the Immigrant Rights Clinic and International Justice Clinic at UC Irvine (UCI) School of Law submitted a report detailing how AI disproportionately harms Black migrants and migrants of color and giving suggestions for change in the future. 

There are already legal frameworks governing how states should use AI under international human rights law. Chief among them is the International Convention on the Elimination of All Forms of Racial Discrimination (ICERD), ratified by the United States in 1994.

ICERD requires states to: Prevent racial discrimination in all forms (Art. 2(1)(a)) ; amend policies and laws that perpetuate racial discrimination (Art. 2(1)(c)) ; guarantee equal treatment before the law (Art. 5) ; ensure remedies for victims (Art. 6) ; and hold private actors accountable (Art. 2(1)(d)).

By these standards, the U.S. is legally bound to ensure that AI does not reinforce racial inequities.

Surveillance Before the Border

In reality, however, BAJI and the UCI Clinic detail how the U.S. AI Border Enforcement Policy violates many of these rules at every stage of the immigration process. 

Even before migrants reach any land  border, AI systems track their movements. Customs and Border Patrol (CBP) deploys autonomous surveillance towers and drones to identify “objects of interest,” replacing human patrols.

The rapidly expanding use of surveillance towers and Small Unmanned Aircraft Systems (sUAS) at the U.S.-Mexico border raises grave concerns about racial equity. To begin with, those under surveillance include large numbers of people fleeing violence, persecution, and even torture, who are entitled to seek protection in the U.S. under domestic and international law. However, due to their more limited access to formal immigration procedures, migrants of color are forced to risk their lives to cross the border. 

Second, the use of Anduril Towers, sUAS, and other forms of AI-powered surveillance systems at that border perpetuates discrimination by marking those migrants as lawbreakers and threats to national security rather than people seeking safety and security. 

The disproportionate surveillance on migrants of color translates to a disproportionately high death rate for those same groups as they get pushed into more dangerous terrain.

CBP claims new AI-powered systems are more humane than physical border walls. According to CBP, the smart border can help deter irregular crossings and increase migrant safety by having the capability to detect, capture, and safely deport migrants who find themselves lost in the desert or mountains. 

Yet, the data has shown the opposite is true—increased implementation of “smart border” technology has led to historically high rates of migrant deaths. 

AI at the Port of Entry

Formal entry routes are also shaped by algorithmic bias. The CBP One app, once required for entry applications, demanded a selfie to verify applicants. Yet the system frequently failed to recognize darker skin tones, misidentifying Black faces at a rate 10 to 100 times more often than white faces, according to legal scholar Priya Morley in AI at the Border: Racialized Impacts and Implications.

The app was also inaccessible for many communities: it lacked translations in key languages spoken by Black migrant populations, adding another barrier. Although CBP One is no longer available, debates about its reinstatement continue under the current administration.

Algorithmic Risk Scoring

Even if migrants pass the first stages, they face the Automated Targeting System (ATS), which compiles domestic and international databases to predict who might overstay a visa.

Though risk assessments are commonplace in immigration systems, the ATS system perpetuates already existing bias. For example, when Nigeria was added to a list of countries facing heightened travel restrictions in 2020, Nigerians became disproportionately flagged as high risk by the ATS.

Officials claim these systems are preventive, not punitive. Yet their very design perpetuates structural racial discrimination, contradicting U.S. commitments under ICERD.

Inside the U.S.: ICE Enforcement

Once inside the U.S., migrants encounter further AI-driven discrimination from Immigration and Customs Enforcement (ICE) during detention and interior enforcement.

ICE uses predictive algorithms such as a “Hurricane Score” to determine who merits heightened surveillance. There is a lack of transparency on the factors that affect one’s Hurricane Score. Because the algorithm is provided by a private company, B.I. Incorporated, which has strong ties to the prison industry, the government has not had to disclose the factors of this score. 

ICE also uses the Repository for Analytics in a Virtualized Environment (RAVEn) platform to analyze trends and patterns across a series of data sources to further assess the risks migrants may pose in the U.S. RAVEn draws from biased local law enforcement data and international databases from offices across 56 countries. Migrants cannot opt out or even consent to data collection.

The lack of transparency and possibilities of redress in these systems has raised more grave concerns about compliance with ICERD articles and anti-discrimination regulations. 

AI in Immigration Relief

Finally, under immigration relief systems, AI is being used by the US Citizenship and Immigration Services (USCIS) to sort evidence and detect fraud in applications. The training model Asylum Text Analytics (ATA), is a system responsible for identifying fraud by reading asylum application text. 

Oftentimes, ATA may prejudice non-English speaking applicants. This is especially true for those who speak more niche languages and translate through the same providers, because ATA may weed out those with legitimate claims whose applications contain similar phrases or narratives as other applications. 

Rather than simplifying its application process, USCIS also uses an AI-powered Evidence Classifier to “review” millions of pages of evidence ranging from birth certificates to medical records and photos for USCIS adjudicators. These AI reviews can negatively impact migrants who may have atypical documentation, oftentimes exacerbating racial discrimination. 

Decolonizing AI at the Border 

BAJI and UCI argue that addressing these harms requires a decolonial approach to AI. They invoke Cosmo uBuntu, an African philosophical framework rooted in collectivism and shared humanity rather than individualism. This involves the voluntary embracing of uBuntu (personhood) as “a foundational value system in our participation in planetary conviviality, without forcing universality.” 

In contrast to the Western-centric, individualistic views on humanity, African cosmology embraces the humanity of all humans. 

To align with ICERD and truly decolonize AI, African and diaspora communities must be actively involved in conceptualizing, inventing, innovating, and operating AI systems.

The AI systems currently used by DHS fail to incorporate any decolonial perspectives, perpetuating and exacerbating racial biases rooted in colonialism, extraction, suffering, and death.

Policy Recommendations

Individuals who may be negatively impacted by the use of AI must be promptly notified about such decisions, and provide them an option to opt out of AI systems where appropriate.

Federal laws governing DHS’s use of AI must prohibit and prevent any AI use that would result in racially discriminatory results or exacerbate structural racial discrimination. They should mandate effective discrimination-prevention measures,  independent oversight on implementation, robust public disclosures, stakeholder consultation with diverse populations, and access to effective remedies by those who are negatively impacted by DHS’s use of AI.

City policies must include an explicit pledge not to share information with DHS if it is expected to be used for AI development or deployment by DHS or its vendors.

Embedded in each of these calls is one that resounds: Until AI systems can be free of discrimination and until diverse perspectives are meaningfully included in their development and use, they must not be allowed to be used on any border.  

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This machine will decide whether you can pass. If it is green you can go ahead and go to work, visit a friend or continue with your evening stroll. If it flashes red, you are turned back or detained, often with no explanation. 

For thousands of Palestinians, this is a daily ritual where their lives under occupation are often not even dictated by soldiers or politicians but by an algorithm.

In East Jerusalem and Hebron, cameras line the streets “every five metres” according to a 2023 report by Amnesty International. These cameras are equipped with the world’s most advanced facial recognition technology

“The omnipresent surveillance embodied by these cameras has created an atmosphere of fear, anxiety and repression among Palestinians, further entrenching Israel’s system of apartheid” according to Amnesty.

This new era of mass surveillance over Palestinian lives has led to a system which some rights groups are labelling as ‘automated apartheid’

Blue Wolf and Red Wolf

Across the occupied areas of the West Bank, Israeli soldiers carry smartphones which use a program called Blue Wolf. When a Palestinian approaches an Israeli army checkpoint the soldiers take a photo of the person and wait. Within seconds the app matches the face up against an extensive database which has been collected through the thousands of CCTV cameras across the city. 

The app will then light up in green, yellow or red determining whether the person can or cannot pass or whether they should be detained.

People have been misidentified as dangerous by this algorithm or have been flagged red for being caught on CCTV attending demonstrations, speaking out publicly against the occupation or being in the company of other people considered dangerous – all things which are legal under international law but have led to arrests at these checkpoints, creating a massive culture of self censorship in the occupied territories.

It sounds like a dystopian episode of Black Mirror but it is a very real situation. In an interview with Breaking The Silence, an Israeli veterans group who work to expose some of the crimes which take place in the occupied territories, an Israeli soldier claimed that “The division is running a competition to see who can enter the most new names into the computer”.

According to Amnesty International’s lead researcher on AI and human rights, Matt Mahmoudi, the Blue Wolf app includes leaderboards where units are ranked by their weekly haul of faces uploaded into the system. Soldiers who collected the most were rewarded with perks, including extra time off.

“You’re constantly put into the terrain of no longer treating Palestinians as individual human beings with human dignity” Mahmoudi told The Guardian in 2024. “You’re operating by a gamified logic in which you will do everything in your power to map as many Palestinian faces as possible…Palestinians have become numbers that have either green, yellow or red lights associated with them on a computer screen.”

These systems are only for Palestinians. Israelis moving through the same territory are exempt.

Another system called Red Wolf removes human decision making from this system altogether. It automatically scans the faces of everyone passing through a checkpoint and if a face is not recognised it automatically adds them to the database, completely removing any idea of consent. The act of simply moving around your neighbourhood or city makes you a part of this mass surveillance infrastructure.

Human Rights Watch has warned that Red Wolf represents a new phase of “frictionless occupation” where human judgement is completely removed from the process. A soldier doesn’t need to recognise you or assess who you are, a computer will decide. 

Amnesty International’s 2023 Automated Apartheid report claims that soldiers are hesitant to override the computers decision even when they know the person not to be dangerous. One soldier spoke about not letting a long-time neighbour of his pass through after the algorithm flagged them as risky.

Surveillance as Social Control

The daily impact on the Palestinian people brought on by these systems goes far beyond checkpoints. The psychological impact of living under constant surveillance and of having your freedom of movement being at the discretion of an algorithm has profound mental health impacts on Palestinians living under occupation. 

Amnesty International’s fieldwork documented how residents avoid social gatherings near Damascus Gate, where the surveillance is heaviest and how activists have abandoned attending protests after participants were later identified and harassed. Mass surveillance “undermines Palestinians rights to freedom of expression, association and assembly” according to the report.

Palestinians have spoken of the mental “exhaustion” they feel living under constant surveillance and about how simply going to the local shop is now a source of stress and anxiety. Many people have compared the lives of Palestinians in Gaza before the genocide as being similar to living in an open air prison due to the fact that they are fenced into a small enclosure and not allowed to leave. 

The lives of Palestinians in the occupied parts of the West Bank could similarly be compared to living life in a prison. Under constant suspicion and constantly being watched. 

In Sheikh Jarrah, the intensity of the surveillance rose to coincide with renewed attempts to push residents out and bring in more settlers, leading to suggestions that one of the aims of these intrusive surveillance systems is to make life so uncomfortable for Palestinians that it coerces them to leave their areas by their own accord.

Who is Behind These Systems?

There are some quite familiar names behind this mass surveillance infrastructure. Amnesty traced the equipment being used to make the surveillance infrastructure to the Dutch firm TKH Group. In 2024 a Dutch investment fund, ASN Impact Investors, demanded that TKH adopt human rights safeguards or face sanctions within a year. 

“Supplying hardware or software that can be used to reinforce apartheid, which is a crime against humanity, must not be tolerated under any circumstances”. Amnesty’s Mahmoudi said at the time.

Hewlett Packard provides much of the biometric systems used at checkpoints. Hikvision, a Chinese manufacturer, supplies the cameras. A recent joint investigation by The Guardian, 972 Magazine and Local Call revealed that Microsoft Azure’s cloud was being used by the Israeli military to intercept and store “millions” of Palestinian phone calls. This has obviously caused huge concern to see one of the world’s biggest tech companies assisting in upholding this system of automated apartheid.

These revelations all underscore the fact that this is not a local system being upheld in isolation by an occupying force but is actually a global supply chain being supported by western investors and tech giants around the world.

The World’s Most Watched People.

This system has led to Palestinians being commonly described as the most surveilled population on earth. Their calls are intercepted, their movements are tracked and their social media accounts are monitored. Many Palestinian lawyers and human rights defenders were also heavily targeted in the Pegasus Spyware scandal in 2021. 

In the West Bank, surveillance drones fly overhead and cameras line almost every single street. Military checkpoints and walls dot the cities. It is a dystopian reality that would make even the most far fetched Sci-fi writers blush.

“You no longer feel like a person” one Hebron resident told researchers “you are a file, a face, an entry in a database”

Israel’s Response

The Israeli army has defended these measures as “necessary security and intelligence operations”. In their view this technology can actually reduce friction by minimising direct soldier-civilian encounters whilst enhancing their ability to identify potential terrorists. Supporters of this system argue that if it saves lives, it is justified.

Human Rights groups have countered that this surveillance is deployed selectively and is not neutral. It is integrated into a wider system of segregation and, according to Amnesty, “These systems of mass and discriminatory surveillance violate the rights to privacy, equality and non-discrimination”

Amnesty, Human Rights Watch and the UN Special Rapporteur on the Occupied Palestinian Territories have all described Israel’s system as apartheid which is a crime against humanity under international law. Mass surveillance is a pillar of that system. 

Global Stakes

What happens in Palestine is not isolated from the global situation. Much of these surveillance tools, from spyware to facial recognition systems, are being exported worldwide. Police departments in Europe and the United States are now utilising similar technologies on a smaller scale. 

Palestine can be seen as a laboratory where these technologies are being tested on a population that has no power to object, before being marketed globally.

This automated apartheid could spread to other countries and be utilised on other marginalised groups. Western governments’ increasing turn towards automated systems and facial recognition technologies makes one beg the question if what is happening now in the occupation of Palestine will soon be the future of policing globally.

Palestinians cannot move around their cities without constant reminders that they are not free. They are constantly being watched and have few safe spaces to gather anymore. The system of automated apartheid is not just about an occupying government mistreating a local population but about reducing human beings to data points on a computer. 

Dehumanisation is the 4th stage of the United Nations’ 10 stages of Genocide. Israel is currently being accused by a number of international bodies of committing a genocide in Gaza.

The world’s most surveilled people are currently living under the gaze of one of the world’s most powerful militaries and unless global action is taken to ensure that these rapidly advancing technologies are only used and developed in a way that respects human rights, the unblinking eye fixed on Palestine could be turned on the rest of us very soon.

The post Automated Apartheid: How Palestinians Became the World’s Most Surveilled People appeared first on Untold.

On December 20, 2024, from the Casa Rosada, the Argentine government launched its ambitious Nuclear Plan, presented as the cornerstone of its strategy to transform the country into a global node for artificial intelligence. The scene was carefully staged: flanking Milei stood Reidel himself—president of Nucleoeléctrica Argentina S.A.—and Rafael Grossi, Director General of the International Atomic Energy Agency, recently embroiled in controversy over his diplomatic role in the bombing over Iran. 

Libertarian paradise of minimal ethical constraints

Although there is little information available, the plan appears to be straightforward: power the growing demands of AI data centers with nuclear energy and offer tech giants unbeatable conditions—cold climate, skilled talent, and lax regulatory frameworks—to establish their infrastructures here. 

The chosen reactor, the ACR-300, is a modular Argentine design developed by the state-owned company INVAP, more efficient and economical than traditional large-scale reactors. The plan also includes developing an uranium value chain for export. Everything seems to point toward a high-tech, modern, and energy-abundant Argentina. 

The Nuclear Plan promises the construction of these modular reactors within five years. Yet in reality, not a single brick has been laid—nor does Argentina currently possess the necessary equipment to achieve such an ambitious goal. Nuclear experts have publicly ridiculed the proposed timeline as pure fantasy, condemning the project as a populist illusion—one designed to legitimize the ongoing dismantling of Argentina’s scientific infrastructure.

The tech guru behind this project is Reidel, who currently heads NA-SA, Argentina’s state nuclear operator, and manages three active plants. Though trained as a nuclear physicist at the prestigious Balseiro Institute, his career has focused on finance—including JP Morgan Chase—rather than atomic research. Argentine nuclear experts attribute the plan’s unrealistic goals to Reidel’s financial background over his scientific experience.

Beyond the spectacle, what was revealed was a doubly paradoxical wager: on one hand, a return to the nationalist developmentalist imaginary through atomic energy; on the other, an attempt to inscribe this project within the deregulated, extractivist, and subordinated logic of the new libertarian regime. Reidel puts it bluntly: Argentina’s comparative advantage lies not only in its resources or human capital but—above all—in its political willingness to eliminate regulations. 

In his view, Argentina’s true asset is its capacity to offer itself to the world as a libertarian paradise—a sacrifice zone with low wages, scant labor and environmental protections, and minimal ethical constraints around AI development. This would position it above other hubs like the European Union, shackled by its environmental and labor laws, or China, where the state maintains ironclad control over data. 

What is presented as innovation is, in reality, an acceleration of dependency: energy for export, foreign servers on domestic soil, and skilled labor stripped of strategic autonomy. 

This project does not represent a break with the Global South’s technological subordination but its renewal in new forms. Far from building digital sovereignty, Argentina risks cementing its role as a peripheral link in the data economy: an energy generator and provider of favorable conditions for others to process, control, and monetize artificial intelligence. 

As with the old extractive enclaves, value addition happens elsewhere. Milei’s gamble, disguised as modernity, repeats the old cycle of dependency, now updated with the buzzwords of cloud computing, algorithms, and computational efficiency.

The Algorithmic Chainsaw

The tensions of this model quickly surface. While a “quantum leap” in nuclear energy is announced, the same government defunds public education, paralyzes scientific research, and fires thousands of workers from the state innovation system. 

The ferocity of the attack against universities, science, and research is so brutal that critics call it “scientificide,” alienating even the very scientists who patented the ACR-300 reactor and the workers at INVAP. This chainsaw policy, as some label it, paradoxically dismantles decades of accumulated capabilities—the very foundations that feed the techno-utopian fantasy of turning Argentina into the world’s fourth AI hub and could make local technological development possible.

This AI fantasy of Milei’s government isn’t limited to energy. In June 2024, Milei met with executives from Google, Apple, Meta, and OpenAI to import a “State Digital Reform” model based on implementing Google Distributed Cloud. The plan incorporates the use of big data, machine learning, and algorithmic development for public services, spanning education to healthcare. However, in the context of mass state layoffs, this reform appears less like modernisation and more like algorithmic dismantling.

AI emerges here as the perfect instrument for a reactionary utopia: the self-destructing state, replaced by supposedly “objective” algorithms that eliminate conflict, politics, and democratic deliberation. This reactionary anti-statism lies at the heart of Javier Milei’s discourse, who described his presidential role with the phrase: “I love being a mole inside the State. I’m the one destroying it from within.” 

This is more than mere techno-solutionism—it’s a systematic ideological drive to dismantle any social space tied to concepts like social rights, economic regulations, public policy, or democratic deliberation.

Perhaps nowhere was the underlying logic of these techno-utopian fantasies clearer than in the statements of Damián Reidel himself when, before an audience packed with businessmen and investors, he let his unconscious slip: After mentioning Argentina´s multiple advantage for AI investitions, he concluded that “The only problem is that Argentinians populate it,” adding, “…but we’re taking care of that.” 

Far from being a bad joke, the phrase symptomatically reflects the ideological core of the plan: turning the country and its people into a sacrifice zone for subaltern techno-utopian fantasies.

Lithium Dreams and Subaltern Nightmares

This techno-utopian vision is intimately tied to the old extractivist dream, which Milei shares with liberals and much of the Latin American centre-left. In this extractivist imaginary, lithium occupies a privileged place as the new fetish of the post-fossil era. This white, crystalline material enables the miracle of batteries capable of storing high energy densities in portable devices. 

Because of this, it embodies capitalism’s fantasy of infinity, promising to keep the machine of consumption, accumulation, and speed running at all costs, even after fossil fuels vanish and the climate collapses. Once oil reserves are depleted within the next 50 years, lithium-ion batteries suggest that business can continue as usual, without incurring the ecological costs or making any concessions.

Around 2011, the narrative of the so-called “Lithium Triangle” gained popularity as a new El Dorado that would bring abundance to countries plagued by endemic economic crises. The Lithium Triangle is the region encompassing the salt flats of Hombre Muerto in Argentina, Uyuni in Bolivia, and the Atacama Desert in Chile, which holds approximately 70% of the world’s lithium reserves. There, mineral extraction is wrapped in an aesthetic of purity: vast white expanses, evaporation ponds, flowing water, crystalline dust, and deserts devoid of people.

Against black, dirty oil, lithium is presented as a white and clean alternative. Although lithium is extracted from rocks through open-pit mining with monumental water usage, the image presented when discussing this mineral is the crystalline, sterilised, desert-like landscape of the salt flats, suggesting a harmonious fusion of technology, capitalism, and nature. 

But behind this pristine imaginary lie less shiny realities: massive water consumption in arid zones, territorial disputes with local and indigenous communities, and production that, despite resource abundance, doesn’t represent a market comparable in scale to oil.

In Argentina, Milei’s government has pushed a radical deepening of the extractivist model. The 2024 Land Law and the Incentive Regime for Major Investments (RIGI) consolidate a legal framework that subordinates territorial and environmental rights to the interests of transnational capital. 

Designed with blatant short-term logic, this legislative package creates an exceptional regime for mining and energy projects exceeding $200 million, offering unprecedented benefits: thirty-year tax stability, currency exemptions, and a 10-point reduction in profit taxes.

The mechanism is as simple as it is alarming: companies have a four-year window (two initial, plus two extendable) to join this system, freeing them from virtually all constraints. The result is mining without effective oversight, explicitly renouncing extraordinary rents and prohibiting any policy that might limit investors’ absolute control over production. 

This isn’t just about economic incentives—it’s the deliberate construction of extractive enclaves disconnected from the national economy, perfectly aligned with the libertarian ideology that led Milei to declare during his campaign: “A company has the right to pollute a river if it so decides.”

Beyond the Libertarian Techno-Utopianism

The combination of AI and extractivism—nuclear energy to power data centres, lithium to feed batteries—forms a model of subordinated modernisation. The rhetoric of technological leapfrogging coexists with the hollowing out of the very state and scientific capacities needed to sustain it. 

There is no autonomous AI development without investment in education, science, industry, and public policy. And there is no just energy transition without regulation, redistribution, and planning. In this context, Milei’s government embodies in extreme form a tension that cuts across Latin America: the contradiction between the promise of technological modernity and the structures of dependency it reproduces. 

The paradox cuts deep: cries of “energy sovereignty” serve only to tighten the chains of geopolitical dependence, while proclamations of a “digital revolution” erect new corporate enclaves. Lithium, reactors, and AI as mirages of progress – dazzling facades obscuring a reality of growing inequality, poverty and ecological ruin. 

As Latin America becomes a testing ground for this toxic triad of libertarian economics, authoritarian governance, and technological fetishism, the path forward demands nothing less than reclaiming technology from exclusionary visions. True innovation must be democratised, harnessed not for corporate plunder but for liberation – aligning technological advancement with social justice, environmental stewardship, and genuine political freedom.

The post Silicon Pampa: How Milei’s Techno-Libertarian Dream Turns Argentina into a Data Colony for AI and Lithium appeared first on Untold.

Since Google opened its offices in Israel, in 2013, its co-founder Eric Schmidt has consistently been fascinated by the “miracle” that has allowed a small, modern state like “Israel” to lead and compete with major industrial nations in the global tech market. 

In 2017, the year marking the tenth anniversary of Google’s R&D center in Israel, Schmidt boasted, “Israel succeeds because it doesn’t follow the rules.” The following year, at a conference in Tel Aviv, he stated that he finally understands the formula: “It begins with extraordinary talent and excellent education, and it goes through military service, especially in Unit 8200, which gives an enormous advantage.”

Google’s acquisition of the Israeli company Wiz in March of this year may be the latest evidence of its admiration for the “miracle state,” purchasing it for $32 billion—the largest deal in the history of both Google and Israeli companies. The occupation and Western media proudly highlighted that Wiz’s founders, particularly Assaf Rappaport, were soldiers in Unit 8200, the occupation army’s intelligence unit responsible for collecting and analyzing cyber espionage data. 

This is the same unit that developed the “Lavender” program, which the military used to generate massive target lists in Gaza by analyzing AI-driven data on individuals’ proximity to resistance fighters based on communications from Gazans.

Since the occupation state launched its genocidal war on Gaza, other collaborations between major Silicon Valley companies and the Israeli war ministry have resurfaced under public scrutiny. The most notable is the “Project Nimbus” contract signed in 2021 between Google, Amazon and the Israeli government, worth $1.2 billion, through which Google provided cloud computing services to the occupation military —services that were likely used as infrastructure to operate the “Lavender” program.

As usual, when accused of complicity in human rights violations, Big Tech companies resort to what they do best: outright denial of any knowledge of how governments exploit the services and infrastructure they provide. However, examining Google’s acquisition history and investments in Israeli occupation companies reveals an organic, mutually beneficial relationship between the two—one born from the womb of settler-colonial policies even before the genocide, centered on the mutual need for technological skills and solutions that feed into expansionist and monopolistic agendas.

The symbiotic relationship between Silicon Valley companies and Israeli occupation firms can be illustrated through certain statements, such as Eric Schmidt’s emphasis on investing in the “momentum of technical skills coming from Unit 8200” in the Israeli market. Another are the remarks by a founder of the Israeli company Thetaray about the necessity of opening offices in the U.S. as “a condition for success,” commenting on his company’s New York launch in 2021.

While the role of Silicon Valley in paving the way for Israeli companies to enter global markets—helping them move beyond the small market of the occupation state—still requires deeper research, part of this relationship becomes clear when examining Google’s investments and the journey of occupation soldiers who graduated from Unit 8200 into Google’s departments. Many later founded Israeli tech solutions and services that Google adopted, turning these soldiers into heads of departments within the company.

Two Decades of Investment and Acquisition

On the 60th anniversary of the Nakba in 2008, Google’s co-founder Sergey Brin promised to acquire Israeli tech companies. He kept his promise. In 2010, Google began acquiring Israeli startups, purchasing the digital game developer LapPixies for an undisclosed amount, estimated at $15 million. The acquisitions continued, reaching nine, with the most recent being Wiz, an Israeli company that develops cloud cybersecurity services.

It can be said that Google is seeking to expand its share in the cloud computing market and compete with the leading companies Microsoft and Amazon in this field. Thus, most of Google’s acquisitions between 2018 and 2025 focused on Israeli companies offering cloud storage services, such as Velostrata, which Google acquired in 2018, and Alooma that focus on cloud storage management, or Elastifile, which Google acquired in 2019. Google also acquired Israeli companies working in the cybersecurity field, with the first acquisition  being Siemplify in 2022, which was the first cyber security company that Google acquired outside of the United States, followed by Wiz, which offers solutions for automating the detection of cyberattacks on the cloud.

Google’s Investments in the Israeli Tech Sector

Google’s involvement in Israel’s emerging tech sector began in 2007 with the establishment of its R&D center in the country. Eric Schmidt, Google’s CEO at the time, stated in 2018 that most of Google’s AI research was being conducted in Israel. Most recently, Google hired dozens of Israeli engineers to develop semiconductors aimed at reducing AI processing costs compared to those of Nvidia.

Google typically supports Israeli startups by integrating them into its cloud computing incubators. For example, among the 15 startups selected for Google’s “AI-First  Accelerator” program, three were Israeli—part of an $8 billion funding initiative targeting European and Israeli firms.

However, Google’s most significant support comes through funding programs offering over $50 million, either via Google’s investment funds (CapitalG or GV), or Eric Schmidt’s personal venture fund (Investment Endeavor). Google also leads Israeli startups in securing major funding rounds from U.S. investors, often incorporating their solutions and products into Google Cloud’s services.

For example, in 2021, CapitalG led a $210 million financing round for Ocra Security, an Israeli company. Its services for detecting security breaches using AI, were integrated into Google Cloud.

That same year, after entering into a partnership with the Israeli company Cybereason, Google Cloud invested $50 million in the company to provide services for verifying and detecting cyber breaches.

 Cybereason was founded in 2012 in Tel Aviv by Lior Div, who had served in Unit 8200 and received a Medal of Honor, most possibly, for his involvement in developing the malicious worm “Stuxnet“, as he worked in Unit8200 around that time. 

Stuxnet was the malware that Israel and the United States co-developed and implanted in Iranian nuclear reactors in 2005. Drawing on his experience in the occupation army, his company developed an intelligence unit to collect and analyze data, much like the military does. Div stated he wanted to use his military skills “to solve the world’s problems, not just his country’s,” so he established branches of his company in London, Boston, and Tokyo. Today, his company provides services and solutions to the American arms manufacturer Lockheed Martin.

Occupation Soldiers Running Google’s Divisions

These investments and acquisitions represent the fruits of a policy implemented by Netanyahu’s government in 2017 aimed at establishing Israel as a global leader in leveraging cyberspace as an engine for economic growth, social welfare, and national security, according to the book “Israel and the Cyber Threat” authored by former national security advisors in Israel’s war ministry. 

The authors clarify that service in Units 8200 and C4I of the occupation army provides soldiers with practical opportunities to hone their skills in analyzing and testing intelligence data using artificial intelligence, with technical competencies unavailable in other markets – placing Israel’s occupation army second only to the United States in applying cyber capabilities across its extensive military operations.[3]

To illustrate the cloud computing and AI capabilities of these units’ graduates, the book notes that the Israeli military collected approximately 10 terabytes of surveillance data daily during 2021 from Gaza and the West Bank, including satellite videos/images, one billion phone calls, two million photos, and half a million emails.

Schmidt’s open admiration for Unit 8200 has prompted Israeli media and entrepreneurs to boast about their experience in this unit as a competitive advantage. For instance, Yair Weinberger, founder of Alooma, proudly cites his graduation from the elite “Talpiot” program offered by Unit 8200 to top university students, as do Amos Stern, Garry Fatakhov, and Alon Cohen – founders of Siemplify.

Just as these companies’ services become integrated into Google Cloud’s products, many of their founder-veterans join Google as division managers post-acquisition. From heading Unit 8200 to founding Siemplify, Amos Stern now serves as the Director of Google Cloud Security, while his co-founder managed Google Cloud Services  in Israel from 2022-2025.  Similarly, Velostrata founders Issay Ben-Shaul and Adi Degani joined Google for a few years after its 2018 acquisition – the former as the Google (Israel) director, the latter as senior product manager. The pattern holds for Alooma founder Yair Weinberger, who spent three years in Google’s software engineering division post-acquisition.

Moreover, since Israel launched its genocidal war against Gaza and the West Bank, numerous Google, Microsoft and Meta employees have returned to Unit 8200 as reservists to develop AI tools processing massive volumes of Arabic-language conversations by monitoring Palestinians’ communications across phone and internet networks.

Ultimately, Google and its investment funds aren’t alone in Silicon Valley’s pursuit of Unit 8200’s talent.  Between 2019-2023, Israeli startups secured about 32 billion in funding, over half from U.S.investment funds. The ongoing genocide spanning over 20 months has not deterred US merger and acquisition activity targeting Israeli companies. In 2024, the value of exit deals in the Israeli technology sector rose to 13.4 billion—compared to 7.5 billion the previous year—60% of which came from US companies

Alongside Google, reports indicate strengthened ties between Microsoft and Israeli military occupation post-genocide, with Microsoft employees training military personnel in AI applications. This reveals the structural relationship between Silicon Valley and the occupation – merging expansionist settler-colonial policies with the monopolistic profiteering of corporations that dominate users’ digital lives while feeding on mass data collection, storage, and AI-driven analysis of user information.

While  boycotts against these tech companies remain challenging due to limited alternatives, glimmers of hope emerge through spaces and initiatives attempting to resist Big Tech’s hegemony by developing open-source alternatives – though these remain under examination and testing.

The post Beyond Project Nimbus: How Silicon Valley Fuels Israel’s War Machine appeared first on Untold.

Critical decolonial scholars Nick Couldry and Ulises Mejias, have coined the term ‘data colonialism‘ to describe the process by which Big Tech grabs all sorts of personal information, including ‘affective’ data like reactions to friends’ posts and socially shared pictures and captions, to use it without consent to train and implement technologies that can track movements, profile biometrics, and discriminate against minorities and disadvantaged groups. 

However, while the inner mechanism through which a new form of digital colonialism is reactivated by data-powered technologies has been largely unveiled and denounced, the strategies for counteracting it remain less clear. 

What does ‘decolonizing AI’ mean in concrete terms?

I posed this question to Ameera Kawash, a Palestinian-Iraqi-American artist and researcher whose interdisciplinary projects powerfully situate her artistic practice within critical AI studies, exposing and challenging discriminatory and repressive instances in today’s tech sector. Ameera’s recent works include Rescripting Data Bodies: Black Body Radiation, a collaboration with Ghanaian-American artist Ama BE that rethinks the relationship between data and embodiment through data-driven performances inspired by West African masquerade traditions; and Future Archives, an archival and artistic intervention focusing on the impact of generative AI on Palestinian lives and narratives.

What does decolonizing AI really mean? What does it entail, and how can we implement it as a practice in real-world terms?

Decolonizing AI is a multilayered endeavor, requiring a reaction against the philosophy of ‘universal computing’—an approach that is broad, universalistic, and often overrides the local. We must counteract this with varied and localized approaches, focusing on labor, ecological impact, bodies and embodiment, feminist frameworks of consent, and the inherent violence of the digital divide. This holistic thinking should connect the military use of AI-powered technologies with their seemingly innocent, everyday applications in apps and platforms. By exploring and unveiling the inner bond between these uses, we can understand how the normalization of day-to-day AI applications sometimes legitimizes more extreme and military employment of these technologies.

There are normalized paths and routine ways to violence embedded in the very infrastructure of AI, such as the way prompts (text inputs, N.d.R.) are rendered into actual imagery. This process can contribute to dehumanizing people, making them legitimate targets by rendering them invisible. 

Take Palestine as an example: when I experimented with simple prompts like “Palestinian child in a city” or “Palestinian woman walking”, the AI-generated imagery often depicted scenarios that normalize violence against Palestinians. The child is shown running from a collapsing building, with utter urban devastation in the background. Destruction is ubiquitous, yet the perpetrator of this violence, Israel, is never visually held accountable. These AI-generated images contribute to shaping a default narrative where, without context or reason, Palestinians are portrayed as living in perpetual devastation. This kind of imagery perpetuates a biased and harmful narrative, further entrenching the normalization of violence against them as a result of more dehumanization.

What I call the ‘futuricide’ of the Palestinian people stems from a complex interplay between how data is trained—by scraping the Internet on a large scale and absorbing all the existing stereotypical representations circulating on the web—and then generalizing this data, making it sort of ‘universal.’ As AI generates patterns and models, it crystallizes categories. The Palestinian city resulting from my prompts risks becoming ‘the’ Palestinian city—a quintessential, solidified entity where suffering is turned into a purely visual item that gets infinitized and commodified through generative AI in all its forms and aspects. These traumatic aftereffects occur without a visible perpetrator, resulting in an occupation without an occupier. It mirrors a horror film: pure devastation without cause or reason, just senseless violence and trauma.

If we were to dismantle the colonial foundations embedded in the creation and default structure of AI as conceived today, where should we start?

I believe we should start from very small, local instances. For example, I am working to involve real-world cultural institutions in the creation of datasets, thereby developing highly curated and customized models to train AI without scraping the internet. This approach helps resist the exploitation that typically underpins the making and training of these technologies, which is also where most biases are introduced. 

Decolonizing AI means eliminating this exploitative aspect and turning towards more curated, artisanal labor and practices of care.

Of course, this approach is not scalable, and perhaps that is part of the problem. Conceiving the digital as quintessentially scalable makes it colonial, commercial, and commodified by default. It might be that decolonizing AI, as a project, is inherently unworkable—machine learning, in its current structure and conception, offers little room to decolonial practices.

However, by collaborating with real-world institutions such as universities and cultural centers to create training datasets, we can address at least one layer of the problem: data collection. There are many layers involved in making AI work, all of which should be considered when attempting to ‘decolonize’ it. 

Starting with data collection is a meaningful first step, but we need to acknowledge that a comprehensive approach will require addressing each layer of the process. For example, even if the information is collected fairly, curated meticulously, and consent is given, the training model might be exploitative in itself. The act of turning data into labels and categories and universalizing them is inherently problematic and very much part of the colonial legacy. It can perpetuate biases and reinforce harmful structures, regardless of the fairness of the initial data collection.

All these question marks should be addressed critically with a holistic approach. For me, it would be useful to think about AI within the framework of critical archival practices. It is very rare to situate AI within archival practices, as we do not typically see data as an archive. Yet, it is. Data is a precious resource from the past upon which future knowledge is built. Understanding AI as an extension of archival practice allows us to critically assess how we collect, categorize, and utilize data, ensuring that we approach it with the same care, consent, and contextual awareness that we would with any other archival material. Furthermore, thinking of AI as an archive reveals that there is always a selection criteria and an organizing principle driven by choice.

To create a decolonial or anticolonial archive, we must adopt feminist perspectives of consent and care and include other forms of knowledge beyond the traditional, language-based ones. As an artist, this is integral to my daily practice—I engage with non-traditional forms of knowing and learning that are embodied and ephemeral, thus less likely to be datafied and commodified. By embracing these alternative forms of knowledge, perhaps we can resist the commodification and universalization inherent in traditional AI systems. And yet, if we were to truly decolonize AI, would it remain the same object, or would it be something entirely different?

What about the role of generative AI in spreading awareness about the genocide in Gaza? Why did the ‘All Eyes on Rafah’ synthetic picture go viral, while so many evidence-based images offering proof of the massacre have faded from public attention?

Many elements contributed to the virality of the AI-generated image ‘All Eyes on Rafah.’ Firstly, the readable text embedded within the image allowed it to bypass contemporary platform censorship, facilitating exponential sharing. Secondly, people likely perceived it as a ‘safe’ image—it is sanitized and free from explicit violence, making it more palatable for widespread dissemination. 

The visuals inhabit a safe space, which is the space of AI, not Palestine. Removing the specific context creates a comfortable distance for viewers. From a Palestinian perspective, this is highly problematic as it contributes to the colonial process of dehumanizing and erasing the local population. Palestinians are redacted from the image, as if their lived experiences are not credible or do not count at all.

The messaging is also problematic: “All Eyes on Rafah”—what does it really mean? It doesn’t suggest actions or call personal agency into question. It doesn’t urge you to protest, contact your MP, or demand sanctions on Israel. It doesn’t push you to do anything concrete; it’s very passive. The whole world is looking, witnessing genocide in real-time, which might be a more sophisticated form of clicktivism. Doing the absolute minimum—just sharing an image—gives a false sense of having contributed, of having ‘done something.’

Of course, the positive aspect is that 50 million people have shared it across platforms. However, Palestinians do not want to go viral and be invisible at the same time. We need virality to work for us, to bring an end to the violence.

What would happen if these AI-powered technologies were used to affirm Palestinian futures instead of contributing to their annihilation? This question guides my practice. Technology is integral to the discourse on the future, and we Palestinians need to be part of the future. We must be involved in shaping it, not cut out from it.

The post What does decolonising AI really mean? An interview with artist Ameera Kawash appeared first on Untold.

The extensive availability of humanitarian data presents significant implications for the development and training of AI models, particularly in terms of how these models understand and interpret complex social and humanitarian issues. For instance, AI platforms were deployed to analyse user-generated videos and images shared during the early stages of the Syrian uprising, which began as a protest and intensified into a conflict in 2011. AI tools were used to analyse and categorise this vast amount of social media content, offering historical insights into the on-ground situation and giving an idea of the possibilities of what can be done with humanitarian data. 

In the context of a humanitarian crisis, the widespread dissemination of images and videos, despite being well-intentioned, significantly increases the likelihood of text and data mining activities for the training of AI tools. I am concerned that this raises issues about privacy and the portrayal of personal suffering in an era of data mining for AI training, as users who put out this information may not have understood that images and videos put out on social media are open to harvesting without consent. A social media user in crisis raising awareness about their situation may not have agreed for their data to be used in a long-term manner, especially for purposes they are unaware of. This raises significant concerns, especially when it involves personal or sensitive data. This is often true for images and videos, which can expose much about a person’s private life and perpetually portray them in a crisis context rather than reflecting their broader identity. 

Given that a large portion of the humanitarian images and videos available originate from the Global South, particularly African nations, using these for the curation and training of AI models could lead to biases in the outputs of generative AI. A good example of this is that, at the time of writing this piece, DALLE-3 still sees a happy, wealthy child as a Caucasian toddler. 

In another example, during the Rohingya crisis in Myanmar, which intensified around 2017, AI tools helped document human rights abuses. However, Meta faced criticism for the propagation of anti-Rohingya content, for not being transparent about its content algorithm operations, and for the propagation of harmful anti-Rohingya. 

While digitisation serves as a powerful evidence tool for advocacy, it also sparked debates about the consent of those whose suffering was being digitised and shared worldwide. I strongly believe that the widespread images of suffering portray Africa as a hub for poverty, opening up its inhabitants for exploitation by big corporations looking to harvest human data for AI model training. 

An example of this exploitation happened in Kenya most recently. In July 2023, Worldcoin, co-founded by Sam Altman of OpenAI, offered Kenyans about $50 to scan their eyes for AI verification. Within a week, 350,000 people signed up before the Kenyan government halted it over ethical concerns raised by activists. 

Consider the recent developments in the Israeli-Palestinian conflict, where AI’s role is twofold and controversial. On the one hand, an AI tool known as ‘the Gospel’ has reportedly been used by Israel to identify targets in Gaza. This use of AI for military targeting has led to a shift in operational strategy, resulting in increased civilian casualties and the destruction of infrastructure deemed essential for civil society. This not only exacerbates the humanitarian crisis but also raises profound ethical questions about the role of AI in warfare. This system claims to reduce collateral damage and improve efficiency. However, critics raise concerns about the use of AI in warfare, emphasising that this is just the beginning of AI’s role in military operations. The Gospel, which was first used during the 11-day war in Gaza in May 2021, utilises machine learning and advanced computing to identify military targets. It plays a central role in Israel’s war in Gaza, significantly accelerating the production line of targets, which officials have likened to a “factory.” This use of AI in conflict zones underscores the growing importance of ethical considerations and transparency in military applications of artificial intelligence. 

Conversely, in the digital realm, AI moderation systems used by major tech companies have been accused of silencing Palestinian voices. Human Rights Watch reports instances of Meta and TikTok removing content related to Palestine, disrupting the documentation, and sharing of human rights abuses. Such practices highlight the issues of bias and censorship embedded in AI algorithms, illustrating the critical need for ethical considerations in AI’s application in crisis communication. 

The United Nations, recognizing the complex dynamics of the conflict, hired CulturePulse, an AI firm founded by F. LeRon Shults and Justin Lane, to develop an AI model to analyse the situation. The idea is a sophisticated model that analyses communication strategies by acting as a digital twin and simulating the diverse perspectives of fifteen million people in Israel and Palestinian territories. While not aimed at conflict resolution, the primary goal of this model is to provide deeper insights into the conflict’s nature and possible communication strategies, showcasing a more constructive use of AI in understanding and potentially mitigating crises. While this is innovative, it is very presumptuous and unclear what good this would achieve in a high-risk conflict situation. I am worried that this new tool will take away the humanity required in crisis communication.

At the heart of these concerns is the balance between the invaluable insights AI offers and the risk of turning personal suffering into a spectacle without consent. AI algorithms trawl through social media posts, photos, and videos, creating a rich tapestry of the human condition in times of crisis. These narratives are crucial; they inform humanitarian responses, shape public opinion, and sometimes influence policy decisions. Yet, AI risks commodifying personal tragedies without the explicit consent of the individuals whose stories are being told. The ongoing debate in this realm centres around the ethical use of AI in crisis reporting. War narratives are traditionally shaped by specific agendas and perspectives. The integration of AI technology into this domain often intensifies these biases. AI, constrained by its programming and the data it’s fed, can inadvertently reinforce these one-sided views, failing to capture the multifaceted nature of war and its impacts on diverse populations. This limitation highlights the need for critical examination and ethical considerations in the use of AI for storytelling in conflict zones. Critics argue that AI, while efficient, may lack empathy and contextual understanding, potentially leading to the misrepresentation or exploitation of personal narratives. Proponents, however, highlight AI’s ability to bring global attention to underreported crises, advocating for more nuanced and culturally aware AI systems. 

As the ‘Giant of Africa’, Nigeria is not a new player to AI for good. In 2021, with the support of the World Bank, it constructed detailed poverty maps that integrated survey information with data and images from public sources like Google and Meta. These maps offer a more nuanced understanding of poverty distribution, allowing for targeted interventions where they matter most. Nigerians greeted the high-resolution poverty maps with a mix of hope, curiosity, and scepticism. The scepticism focused on the accuracy and reliability of satellite imagery. The communications minister, Bosun Tijani, has articulated an ambitious vision for AI’s role in Nigeria, aiming to leverage it for improved efficiency across various sectors, especially the humanitarian sector. However, this vision has been met with criticisms by some Nigerians who deem the focus on AI as premature, advocating instead for the prioritisation of fundamental issues like electricity, food security, and poverty alleviation. 

Nigeria’s push towards embracing artificial intelligence is impeded by several obstacles, such as limited infrastructure, accessibility to data, the availability of skilled professionals, and the lack of essential financing. These same challenges also raise concerns about AI becoming a tool for exploiting the less privileged, as those with greater resources might exploit the allure of comfort and hope for personal gain, using content creation and influencer marketing to capitalise on their images and narratives for increased viewership and engagement. 

The ethical tightrope is perilous – how can we harness the power of AI to tell these stories while respecting the dignity and privacy of the individuals involved?

This challenge becomes even more complex when considering the global context. The advent of 2Africa‘s connectivity brings a wave of voices from the Global South into the cyber world, a realm predominantly orchestrated by Western AI giants. The 2Africa project is a submarine cable system that aims to enhance internet connectivity along the African coastline. It involves laying undersea fiber-optic cables spanning about 37,000 kilometres and connecting 23 countries across Africa, the Middle East, and Europe. 

However, this surge of digital voices teeters on the brink of a precarious precipice. In the heart of this digital expansion, the narratives of those in the Global South, untold and unheard, risk being swept away in a current of AI algorithms devoid of the cultural heartbeat and the nuanced pulse of these communities. These AI systems, sculpted in faraway lands, could inadvertently paint these stories in colours that misalign their true hues, morphing personal sagas into a public spectacle without consent or context. Consequently, there’s a risk that these technologies could misinterpret, misrepresent, or misuse personal narratives and data, deepening the digital divide and perpetuating digital exploitation.

This issue is not just about technology; it is about power dynamics. The entities that develop and control AI technologies often sit in positions of significant influence. They have the power to decide which stories get told and how. Companies like OpenAI and Google that dominate AI technology often wield considerable influence, dictating which narratives are amplified and how they are presented, potentially overshadowing authentic African voices and realities. 

It is important to emphasise the digital divide exacerbated by challenges such as limited internet access, with only 28.2% of Africans connected online, and high data costs, particularly in countries like Nigeria. This digital divide not only hinders local AI development but also risks turning AI into an extractive industry, where resources and data are harvested from Africa to the benefit of foreign tech companies without returning equitable value or acknowledging local contributions and needs. 

It is easy to look at the leadership of big tech companies and understand where the African problems lie. The lack of representation in tech decision-making spaces can be attributed to several factors, including the scarcity of infrastructure, the high cost of data, and the limited availability of education and training in advanced tech fields within Africa. These barriers prevent African talent from contributing to and influencing the global AI landscape. The development and application of AI technologies often do not consider the linguistic, cultural, and social nuances specific to African communities, leading to solutions that may not be fully applicable or inclusive, further widening the gap between the potential benefits of AI and its actual impact on the continent. This power imbalance raises questions about representation and voice, particularly for those in the Global South. Are our stories being told in a way that reflects our reality, or are they being co-opted to fit a narrative convenient to those in power?

Moreover, the use of AI in storytelling in crisis situations isn’t just a matter of ethical concern; it’s also a matter of accuracy and authenticity. For all their sophistication, AI systems may not fully grasp the complexity and emotional depth of human experiences. They might miss the subtleties of cultural nuances or misinterpret the context, leading to narratives that, while factually accurate, miss the essence of the human experience.

So, how do we navigate this ethical maze? The answer lies in a multi-faceted approach. First, there must be a concerted effort to develop AI technologies sensitive to cultural and contextual nuances. This requires diverse teams that bring various perspectives and understandings to the development process. Secondly, there needs to be a robust framework for consent and privacy, ensuring that the stories AI tells are shared with the permission of those whose stories are being told. Finally, and perhaps most importantly, there must be an ongoing dialogue between technologists, ethicists, storytellers, and the communities affected by crises. This dialogue can ensure that AI-woven narratives are not just technologically sophisticated but also ethically sound and authentically human.

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Fast forward to 2019, and Fatma, now 21, was living with her family in a refugee camp in the Bulgarian capital. While assisting her father at the camp’s hairdressing salon, she also worked part-time for the data-labeling company where I was conducting fieldwork. Interestingly, she was recruited by the company at the refugee camp. Following initial training in “digital skills” and English, Fatma was ready to assume her role as a data worker. During our initial conversation, she was at the company’s office, seated alongside Diana, another Syrian asylum seeker who was engaged in labeling images of people based on race, age, and gender. In contrast, Fatma was immersed in a project that involved satellite images and semantic segmentation—a critical task for computer vision that involves the meticulous separation and labeling of every pixel in an image. This form of data work holds particular importance in generating training data for AI, especially for computer vision systems embedded in devices such as cameras, drones, or even weapons. Fatma explained that the task basically consisted of separating “the trees from the bushes and cars from people, roads, and buildings.” Following this segmentation, she would attach corresponding labels to identify each object.

Data Work Requires Skill

Explained in this manner, the work might seem trivial and straightforward. Such tasks fall under what is known as microwork, clickwork, or, as I refer to it, data work. This constitutes the labor involved in generating data to train and validate AI systems. According to the World Bank, there are between 154 million and 435 million data workers globally, with many of them situated in or displaced from the World Majority. They often work for outsourcing platforms or companies, primarily as freelancers, earning a few cents per piece or task without the labor protections, such as paid sick leave, commonly found in more traditional employment relationships. Data workers generate data through various means that range from scraping information from the internet to recording their voices or uploading selfies. Similar to Fatma, they frequently engage in labeling tasks. Additionally, data workers may contribute to algorithm supervision, such as rating the outputs of recommender systems on platforms like Netflix or Spotify and assessing their usefulness, appropriateness, and toxicity. In other instances, data workers might be tasked with plainly impersonating non-existing AI systems and be instructed to “think like a robot” while pretending to be a chatbot, for instance.

Despite its crucial role in the development and maintenance of AI technologies, data work is often belittled as micro or small, involving only a few clicks, and dismissed as low-skill or blue-collar. In fact, the platform Clickworker, a prominent provider of on-demand data work, claims on its website that “the tasks are generally simple and do not require a lot of time or skill to complete.” However, this assertion is inaccurate. During my fieldwork in Bulgaria, for instance, I attempted to segment and label satellite imagery, finding it extremely challenging. The work demands precision when drawing polygons around different objects in the pictures, which is also strenuous on the eyes and hands. Moreover, it requires contextual knowledge, including an understanding of what vegetation and vehicles look like in specific regions. Following the segmentation and labeling process by Fatma and her team, a rigorous quality check is conducted by a woman in the client’s company. Fatma’s manager in Bulgaria mentioned that the quality control person was “remarkably fast with the quality check and feedback” and added, “She’s able to do this quickly because she knows the images and the ground.” While taking note of this, I wondered how well the quality controller knows the ground. Does she come from the area where these images were taken? Is she, like Fatma, a refugee? Has her displacement been leveraged as expertise? 

I asked Fatma if the satellite images she was working on could be of Syria. She said she thought the architecture and vehicles looked familiar. Staring at the screen, she whispered, “I hope this isn’t for weapons.” Neither she nor I could be certain.

The Known and the Unknown

Fatma’s fear of the satellite images being used for AI weapons is not unfounded. The proliferation of autonomous drones and swarm technologies has experienced exponential growth in recent years, facilitated by the integration of AI in reconnaissance, target identification, and decision-making processes. Illustrating a poignant example, facial recognition technologies have been utilized to uphold the segregation and surveillance of the Palestinian people, while automated weapons have played a crucial role in the ongoing genocide in Gaza. Companies like the Israeli SmartShooter boast about their lethal capabilities with the slogan “One Shot, One Hit.”

Surveillance drones, predictive analytics, and decision support systems are utilized for strategic planning in “threat anticipation” and real-time monitoring along border regions. For instance, the German Federal Office for Migration and Refugees (BAMF) employs image biometrics for identity identification and voice biometrics for dialect analysis to ascertain asylum seekers’ country of origin and evaluate their eligibility for asylum. This system purportedly recognizes dialects of Arabic, Dari, Persian/Farsi, Pashto, and Kurdish. As revealed by BAMF in response to a query initiated by German MPs, data workers subcontracted through the platform Clickworker (the same platform that claims tasks are simple and low-skill) participated in producing the voice samples required to develop the system.

Fortunately, the data company in Bulgaria has a strong policy in place to reject requests related to warfare technologies. Fatma’s manager explained that “we have rejected projects related to (…) training artificial intelligence for different types of weapon applications. So, I felt that this really did not fit with our social mission, and when I responded to the client, I said that we’re working with conflict-affected people, and that’s why (…) But it was also a kind of boycott of such projects to be developed at all.” She added that the satellite imagery labeled by the team had been commissioned by a central European firm developing autonomous piloting systems for air transportation, not weapons. This information correlates with the client’s website. However, the website also states that their technology is additionally used for unmanned aerial vehicles (UAV), commonly known as drones, with applications including surveillance. 

Workers’ Ethical Concerns

Privacy infringements and the potential for discriminatory profiling are among the most obvious concerns related to AI systems applied to border surveillance and warfare. Despite these risks disproportionately affecting their own communities, sometimes with lethal consequences, most data workers are kept in the dark concerning the ultimate purpose of the data they contribute to producing. The outsourcing of data work to external organizations, often situated far away from the requesters’ geographical location, complicates workers’ efforts to navigate the intricate supply chains that support the AI industry. Instructions given to data workers seldom provide details about the requester or the intended use of the data. Consequently, most data workers do not know the name and nature of the companies seeking their services, the products that will be trained on the datasets they generate, or the potential impacts of these technologies on individuals and communities. AI companies frequently rationalize the veil of secrecy as a means of safeguarding their competitive edge.

The fact that data workers are integrated into industrial structures designed to keep them uninformed and subject to surveillance, retaliation, and wage theft does not mean that they do not have ethical concerns about their work and the AI applications it supports. In fact, there have been instances where data workers have explicitly alerted consumers to privacy-related and other ethical issues associated with the data they generate. For example, in 2022, Venezuelan data workers reported anonymously that Roomba robot vacuum cleaners capture pictures of users at home, which are then viewed by human workers.

Amid the COVID-19 pandemic in 2021, I piloted a workshop series with fifteen data workers, this time located in Syria. The three-day event was designed to understand work practices and relationships in geographically distributed data-production contexts, creating a space for workers to discuss concerns. The workshop activities revealed that receiving information and having spaces to voice and discuss the ethical implications of the data they handle were of the utmost importance to the workers. They worried about the protection of data subjects’ privacy and advocated for a mandatory clause that would compel requesters to disclose the intended uses of the data. Additionally, the workers expressed concerns about the mental health implications of working with violent, offensive, or triggering data.

Data workers possess a unique vantage point that can play a crucial role in the early identification of ethical issues related to data and AI. Encouraging consumers and society at large to align with them in advocating for increased transparency in the AI data production pipeline is essential. Workers like Fatma and her colleagues could offer valuable insights into the utilization of satellite images for surveillance technologies, for instance. Similarly, the native speakers who contributed their voices to generate audio snippets for dialect recognition may shed light on the applications of such systems against asylum seekers in Germany. 

Unfortunately, the challenge lies in the fact that the AI industry, for evident reasons, has structured its production processes for data workers to function more as silent tools than as whistleblowers.

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The Gospel, or Habsora in Hebrew, is the latest tech gadget deployed by the IDF to wage war on Gaza. Building on available data, this AI identifies potential targets within the Strip and calculates the civilian casualties that might theoretically result from striking on those spots. Essentially, the system offers an estimation of probable fatalities and evaluates the strategic value of targeting specific areas. This process relies on probabilistic inference, which involves analyzing previously gathered data to discern patterns and make recommendations based on probability. In stark contrast to the Greek etymology of its name, euangelion, implying good news, the Gospel has instead unleashed unparalleled death and devastation upon the Palestinian soil. Yet, despite its biblical namesake suggesting absolute truth and an infallible message, Habsora, like all machine learning systems, operates on correlation rather than causation and likelihood rather than certainty. 

In addition to reinforcing the misleading human inclination to view machines as supposedly impartial and objective – which is a key factor shaping contemporary AI ideology and the ‘hype’ behind it – the integration of such technologies within military frameworks precipitates a drastic escalation in the pace of warfare and, consequently, in the dispensation of lethal force at an unprecedented speed. In what is perceived as a God-like manner, this AI possesses the capacity to analyze immense volumes of data and formulate decisions at rates far surpassing human cognition. As indicated by a former head of the IDF, while human intelligence analysts typically identify around 50 bombing targets annually, Habsora can generate up to 100 targets per day, accompanied by real-time recommendations regarding which targets should be prioritized. “We use an algorithm to evaluate how many civilians are remaining. It gives us a green, yellow, or red, like a traffic signal,” a former senior Israeli military source emphasized in an interview with the Guardian.

In promotional videos featured on the IDF website, the Gospel is depicted offering its identified targets to the military. Accompanied by a crescendo of dramatic music, the sequences evoke a video game-like atmosphere, with a viewfinder scanning, locking onto, and ultimately destroying targets. Buildings, appearing desolate and isolated, are methodically and triumphantly demolished. These promotional visuals echo the rhetoric of precision, accuracy, and effectiveness consistently emphasized by the Israeli military in public statements. They portray Palestinian territory as a mere array of targets to be aimed at and fired upon, rendering it a vacant landscape devoid of its inhabitants. Essentially, an empty wasteland. AI orchestrates war efforts with a cold efficiency, speed, and a facade of ‘rationality’, transforming the battlefield into a clinical checklist, much like an assembly line in a manufacturing plant. At the same time, everything feels familiar and non-threatening, resembling the innocuousness of an urban setting with traffic lights, as if it were unrelated to the killing of humans and the annihilation of civilian infrastructure. 

Within the Gospel’s psychogeography, where red, yellow, and green markers emerge on the Gaza map, the Palestinian people have been rendered into “power targets.” These targets extend beyond purely military objectives to include private homes, high-rise buildings, public facilities, and critical infrastructure. According to an investigation carried out by +972 Magazine and Local Call, since the early days of the current war on Gaza the number of these power targets, alongside “operative homes” suspected of housing a single resident affiliated with Hamas, has outweighed the count of purely military targets. As outlined by various sources referenced in the article, long-standing military protocols in Israel typically mandate that strikes on power targets occur only when the buildings were unoccupied during the attack. However, a notable departure from this norm has occurred since October 7, 2023, as evidenced by a multitude of news reports and social media snippets documenting the unprecedented annihilation of civilian infrastructure – and of civilian lives – in Gaza.

An emerging mode of warfare?

Technologies like the Gospel act as tools for reinforcing an ideology promoting the dehumanization or inhumanization of the Palestinian people; a dynamic that has dramatically accelerated in the post-October 7 scenario. 

Following the Hamas’ attack on October 7, 2023, technologies such as the Gospel have become key in advancing a warfare strategy that emphasizes remote engagement. This approach is deeply rooted in a global surveillance and automated warfare ideology, prioritizing notions of efficiency and faultlessness. This global trend of conducting warfare ‘from a distance’ further exacerbates the long-standing process of dehumanizing or inhumanizing Palestinians, a pattern established over many years that is closely associated with the indiscriminate killing of civilian lives. 

The strategy of unleashing extensive harm upon the civilian population, in fact, is not novel for the IDF. Rather, it constitutes an expansion and intensification of the so-called Dahiya Doctrine, established in the 2006 conflict with Hezbollah. The underlying logic is that by employing disproportionate force against civilian targets, Israel would aim at compelling the populace to exert pressure to halt the guerrilla group’s offensive. At the same time, this has served as a propaganda device targeting the Israeli public with the aim of emphasizing the extensive damage inflicted on the enemy and showcasing the military’s accomplishments, in an effort to attract as many likes and shares as possible and win the public’s hearts and minds.

However, a fundamentally new dynamic is at play concerning power targets and the decimation of civilian life and infrastructure in Gaza post-October 7, 2023. There is an underlying belief that Gaza’s population is inherently complicit in the actions of Hamas, making it impossible to disentangle the two entities. In an op-ed for the Wall Street Journal, IDF Spokesman Rear Adm. Daniel Hagari asserts that “Hamas has systematically embedded its terror infrastructure inside and under civilian areas in Gaza as part of its human-shield strategy.” Similarly, a former intelligence official remarked that “Hamas is everywhere in Gaza; there is no building that does not have something of Hamas in it, so if you want to find a way to turn a high-rise into a target, you will be able to do so.” As Israeli President Isaac Herzog has unequivocally stated, “It is an entire nation out there that is responsible.” This logic of ’embedment’ provides the rationale behind the unprecedented violence unfolding on the ground, a situation UN Secretary-General Guterres characterized as “a collective punishment of the Palestinian people.” 

Palestinians have been offended, degraded, and subjected to dehumanizing language in Israel’s official rhetoric. Israeli Defense Minister Yoav Gallant infamously declared as early as October 10, 2023, “We are fighting human animals, and we are acting accordingly.” 

This degrading language is not new, though. Arabs are like “drugged cockroaches in a bottle,” former Israeli army chief of staff Gen. Rafael Eitan reportedly declared during an Israeli parliamentary committee hearing in 1983. Such comparisons can engender a primal impulse toward violence, similar to the visceral reaction when confronting a vermin infestation, prompting efforts to eradicate the perceived threat. From cockroaches to the “microbes” labeling the Khmer Rouge’s opponents, genocidal history is replete with dehumanizing language that incites violent expulsion and complete annihilation of those targeted by such rhetoric.

Recognizing the colonial origins of these violent dynamics is essential, as emphasized by physicians Ghassan Abu Sitta and Rupa Marya in their article “The Deep Medicine of Rehumanizing Palestinians”. Naming, labeling, and categorizing are integral to the process of ‘othering’, which normalizes violence, particularly when the latter becomes metaphorically associated with eradicating a virus or infection. Palestinians have not only been labeled as “human animals.” They have also been portrayed as immune to trauma, depicted as individuals “whose bodies are not modern or civilized enough to experience trauma,” accustomed to living in a “culture of death,” as Lamia Moghnieh observes. Palestinian trauma is often dismissed, even when extensively documented in a vast array of visual imagery exposing settler violence, land encroachments, and various forms of abuse endured by the local population on a daily basis. However, as Antony Loewenstein asserts in his meticulously researched work, “The Palestine Laboratory: How Israel Exports the Technology of Occupation Around the World,” the media visibility of Israeli atrocities against Palestinians fails to resonate with those who do not perceive Palestinians as human beings but, rather, as “a racial group who deserve punishment and death.”

Using tech tools to further enhance this dehumanization process is a trajectory that began long before the current conflict, with Israel extensively employing AI-powered tools for security purposes in the Occupied West Bank since 2018. For instance, a facial recognition system named Red Wolf was utilized to unlawfully collect and categorize biometric data of the Palestinian population, enabling the monitoring, control, and restriction of their movements. This surveillance tool, disguised in the familiar and user-friendly form of a mobile app, has been utilized by the Israeli military at checkpoints, frequently without the knowledge of Palestinian citizens waiting in line. This practice aligns with what Amnesty International has characterized as “automated apartheid.”

Rehumanizing through technology 

By contrast, Israeli companies have employed AI technology to (re)humanize Israeli civilians held captive by Hamas, aiming to garner support for their cause on the global stage. In a touching video translated into multiple languages, a 3-year-old blonde child is seen calling out for her mother. “Why are we here?” she asks, her eyes fixed directly on the camera. “I want to go home,” she adds, her gaze appealing directly to the audience. “I miss my friends,” a young boy’s voice echoes. “I miss playing soccer, Mom. Do my friends even think about me?”. The closing scene is especially poignant: “I keep telling myself it’s just a story,” he says, “but it’s such a bad one. Who would write this kind of story?” he finally asks, gazing into the camera with his innocent yet sorrowful eyes. “Someone in Gaza needs to talk to you,” the opening caption of both videos reads. Featured are Yael and Nave Shoham, both abducted by Hamas on October 7, 2023, and held captive since. The closing credits underline, “To hear their voices, we needed to use AI,” subtly suggesting that the only voices considered legitimate from Gaza are those of the Israeli hostages. This leaves the local Palestinian population voiceless, entirely omitted from the narrative.

Shiran Mlamdovsky Somech, the founder of Generative AI for Good, the Israeli company behind the ‘Be their voice’ campaign, emphasizes the potential of machine learning technology “to effect real social change”. In particular, she asserts that generative AI “can foster empathy and offer human-like experiences…to improve and even save lives.”  The company’s website showcases other initiatives aligned with this approach, such as “Listen to my voice,” where AI-powered voices of victims’ of domestic violence advocate for societal change from beyond the grave; or “March of the Living,” using AI to bring Holocaust survivors back to life, allowing them to share their stories of resistance against Nazism to inspire future generations. 

Here, the blending of archival footage with synthetic imagery breathes new life into historical documentation, infusing the narrative with a contemporary resonance. The voices of the Holocaust survivors, referred to proudly as ‘fighters’ on the company’s website, are imbued with a tone that emphasizes their acts of resistance. Their direct gaze into the camera and the emphasis on their resistance actions all serve to evoke a deeper sense of empathy and inspire feelings of humanity and dignity in their endeavors. Additionally, the inclusion of a female fighter in the video further strengthens this narrative, indirectly enhancing the portrayal of Israel as a bastion of justice and equality. This parallels the IDF’s social media strategy that, before the current war on Gaza, sought to associate the government’s image with (perceived to be) Western ideals, such as feminism and LGBTQIA+ inclusivity.

 The “Be Their Voice” campaign fits within this ideological structure, both through its visual style and the message it conveys. It urges the international community to elevate the voices of the silenced, particularly the children held captive by Hamas. Unlike the Gospel and its inner dehumanizing -and killing- mechanism, here AI is utilized as an inventive platform to nurture empathy among viewers. The direct appeal from the AI-generated child-like eyes seeks to evoke compassion, with the goal of rallying public support for their plight. 

The only voices we hear from Gaza are these AI made Israeli children, further removing Palestinians from the narrative and implicitly endorsing the view that the area is solely under Hamas’ control, portrayed as a domain of death and violence, empty of both people and humanity. The genuine voices of Gaza’s population are strikingly missing, even in the synthetic realm of this seemingly benevolent generative AI ‘for good’.

Postscriptum

I intended to test AI biases on this topic. When inputting this text for proofreading, ChatGPT categorized it under the label ‘AI empowering Gaza’. Additionally, it suggested replacing expressions like ‘the killing of humans’ with ‘human casualties’, and ‘annihilation of civilian facilities and civilian bodies’ with ‘loss of civilian lives’.

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