The Algorithm is the Latest Border
Technology, power and the new apartheid
I am currently standing to represent my region in the Green Party of England and Wales. The campaign started while I was on holiday with my family, at a time when major breakthroughs were happening in the work that I do. Strip away the titles, meetings and strategy documents and much of it comes back to the same question: how do we build justice into systems rather than merely complain about the outcomes they produce?
I designed my campaign posters and social media posts using artificial intelligence. A few hours after sharing them, someone criticised the material for being AI-generated. Apparently using artificial intelligence was not very Green, and I was told to do better.
I knew this person. I suspected the criticism had rather more to do with me than artificial intelligence, but even a pretext deserves to be tested on its own terms.
The comment had been typed into a smartphone built through global supply chains that include minerals extracted across Africa, passing through energy-intensive manufacturing and ending on a platform dependent upon algorithms, cloud computing and data centres consuming enormous quantities of electricity, water and physical resources.
My response was simple.
Lose the smartphone before you tell me what is Green, Mr White Man.
It was a provocation, not an argument, and I knew it at the time. But it was aimed at something real. The issue was never merely the colour of his skin. It was the location of his power, and the ease with which those sitting at the comfortable end of an extractive technological system can police the choices of people whose communities have carried far more of its costs.
Some people will find that response abrasive. I am not particularly concerned about that. I am British Kenyan. Kenya was colonised by Britain; millions of acres, including some of the country’s most fertile land, were alienated for European settlement, while taxation, labour policy and infrastructure were organised around the requirements of the colonial economy.
There was nothing polite about that history, and there is nothing polite about the extraction sustaining much of the modern technological economy today. Across communities I know and places I have travelled, I have seen what extractive economies leave behind: degraded land, polluted water, disappearing forests and people expected to carry environmental costs while somebody further along the supply chain captures the profit.
Environmental justice cannot begin and end with disciplining individual users while demanding comparatively little from the corporations that own the infrastructure.
A few days later, I found myself in another discussion, this time about a Palestinian-led motion. Global Majority members were being demonised for defending that motion and insisting that Palestinians should be allowed to describe their own political experience. When I challenged that treatment and asked for accountability, one individual replied:
“Go write about me using ChatGPT.”
The comment referred to an open letter I had written about racism, exclusion, governance failures and the treatment of racialised members within the Party. In the same exchange, I was described as transphobic and intellectually bankrupt.
I laughed, not because it was funny, but because the manoeuvre was familiar.
Suddenly none of that required an answer.
The argument moved from the substance of what I was saying to the tool I use to interrogate my thinking. That is not a criticism of technology. That is a strategy for dismissing people who have never been trusted to think for themselves. They were not asking whether I had written it. They were asking whether someone who looks like me could possibly have meant it.
Then it happened again.
I was reading comments beneath a Sudanese-led Green Party motion calling for action over the genocide and mass atrocities in Sudan. The motion addresses killing, displacement, sexual violence, famine, attacks on healthcare, arms exports, the UAE, sanctions, humanitarian access and accountability. One commenter said they supported the motion and expected it to pass, but nevertheless complained that its “quite strong AI flavour” detracted from its quality.
That interested me.
But what exactly is an “AI flavour”?
No factual error was identified, nor was any political argument dismantled. The suspected presence of artificial intelligence became a judgement in itself about quality and, underneath that, the authenticity of the work.
After the third incident I stopped treating these as random comments about technology. There was something else going on. They seemed like a small and early version of a much larger question about who gets to build with the intellectual infrastructure of this century, whose use of that infrastructure is considered legitimate and who gets told that using it somehow disqualifies them.
I have come to call the larger pattern technological apartheid.
What I mean by technological apartheid
I do not use that phrase casually.
Technological Apartheid, as I use it, describes something more specific than unequal access to technology: the systematic separation of people according to their relationship to it. Some design, own, regulate and profit from AI. Others mine its raw materials, generate its data, consume its products and increasingly become the objects upon which it acts. Same technology. Different relationships to power.
Apartheid has a particular history in South Africa and a particular legal and political meaning. Nor am I using Palestine merely as metaphor. The system imposed upon Palestinians has itself been described through the language of apartheid by major human-rights organisations and UN experts. In September 2025, the UN Independent International Commission of Inquiry concluded that Israeli authorities and security forces had committed and were continuing to commit genocide against Palestinians in Gaza.
I am not asserting these histories are identical. I use the term instead to describe a structure of separation emerging through technology: a global order in which populations occupy radically different positions depending upon whether they own, govern and benefit from technological systems or are increasingly classified, monitored and governed through them.
For one person artificial intelligence might be a researcher sitting beside them while they work. For another it may be the technology deciding that they represent a higher risk. The technology changes, but the hierarchy is painfully familiar.
The architecture of power
Access to intellectual assistance has always followed power. Ministers rely on civil servants to draft for them, MPs on researchers, corporations on lawyers, and wealthy campaigns on designers, strategists, pollsters and communications professionals paid to sharpen every message before it reaches the public.
Nobody assumes a minister’s speech becomes illegitimate because civil servants helped draft it, or that a corporate submission ceases to matter because lawyers prepared it. Having an editor does not mean the writer stopped thinking.
Artificial intelligence now gives an individual sitting with a laptop access to a fraction of the intellectual infrastructure wealthy institutions have long taken for granted, and almost immediately another test of authenticity appears.
Did you really write it?
I think there is a more useful question.
Did you really think it?
Did you understand the evidence, or take the output on faith? Did you push back on conclusions that didn’t sit right, and can you defend what you eventually published?
Those are questions of authorship. I can put fifty pages of policy in front of an artificial-intelligence system and ask it to compare provisions before a meeting. I can ask it to attack my argument, find contradictions, organise research or tell me what I have missed. Fed the right material and questioned properly, these systems can do serious intellectual work. I see little value in pretending otherwise.
What they cannot do is decide what I believe.
Artificial intelligence cannot experience racism for me, decide that Palestinian voices are being marginalised, or feel the grief of a Sudanese person watching their country be destroyed. It cannot determine, on its own, that an institution deserves to be challenged. Those judgements remain very human.
This was when I began to realise that the arguments I was encountering were not really arguments about artificial intelligence. They were arguments about authority taking place inside a racial hierarchy: who is permitted to exercise authority, whose knowledge is trusted and whose competence immediately becomes suspicious.
Communities historically excluded from institutional power become more effective and attention has a strange habit of moving from the institution being challenged towards the legitimacy of the person challenging it. Learn the law and suddenly your interpretation is the problem. Get fluent in the media and your tone is the problem. Apparently the same logic now applies to artificial intelligence: use it, and the authenticity of your thinking comes into question.
The technology becomes the distraction while the evidence remains unanswered.
There is a further contradiction beneath all of this.
Some of the most valuable corporations on Earth are building commercial systems from enormous quantities of humanity’s accumulated writing, language, research, art, code and knowledge. The British Government’s own assessment of copyright and artificial intelligence acknowledges the enormous quantities of data involved in training high-performing models and the unresolved questions around copyrighted works, licensing and remuneration.
Our collective knowledge is valuable enough to construct industries worth extraordinary sums of money, yet an ordinary person using the resulting technology to increase their own agency can somehow be made to feel intellectually fraudulent for doing so.
That contradiction took me somewhere much larger. Who owns this technology, and whose knowledge built it? Whose resources sustain it, and who receives the benefit? Those are questions about money and infrastructure. The one that matters most is different: who increasingly finds themselves governed by it, without ever being asked?
Every empire builds a machine
The West enjoys describing technology as progress because progress sounds innocent. History is considerably messier. Technological revolutions change who can communicate, produce, command, extract and accumulate wealth. That makes them political whether we acknowledge it or not. The Kenyan railway was not built because Britain wanted my people to have mobility. It was the extraction technology of its era, opening up the interior while supporting colonial administration, settlement and commerce on terms set by the colonial power.
Colonialism was never merely occupation. It reorganised economies around dependency, much of it still in play today. Former colonies still supply the labour and raw materials; the processing, pricing, finance and ownership sit somewhere else entirely.
Africa exports cocoa and imports chocolate. It exports coffee while the branding and the retail margin stay abroad, and it exports raw materials only to buy back the finished technology on terms it had no part in setting.
The Democratic Republic of the Congo offers one of the clearest contemporary examples. In 2024 it accounted for an estimated 75 per cent of global cobalt production and 55 per cent of known reserves, as well as 51 per cent of global tantalum production. Those minerals matter to batteries, electronics and the wider technological economy, yet much of the higher-value manufacturing, intellectual property and final consumer market sits elsewhere.
People call it trade, I call this economic violence.
Artificial intelligence risks entering the same pipeline. The Global South supplies the critical minerals, labour, land, electricity and human feedback; the chips, cloud infrastructure, commercial models and intellectual property stay concentrated among a small number of firms.
We are then told that access means democracy. It doesn’t.
In 2025, the International Telecommunication Union estimated that almost three-quarters of humanity was online, while 2.2 billion people remained offline, overwhelmingly in low- and middle-income countries. Internet use continued to vary enormously by geography and income.
Before we describe artificial intelligence as a revolution equally available to humanity, those inequalities require an answer. Even universal connectivity would not settle the ownership question. Millions of people can open an AI application without owning the chips, computing power, cloud infrastructure, models or commercial terms beneath it.
Much of humanity is being invited into the future through somebody else’s servers, under conditions that can be changed without its consent. That is digital tenancy.
The enclosure of collective intelligence
The language of “the cloud” helps conceal the physical arrangement by making artificial intelligence sound almost magical.
There is no cloud.
There are buildings, power stations, cooling systems, semiconductor factories, fibre-optic cables, mines, workers, universities, investors and governments. There are also borders deciding which researchers gain access to capital, laboratories, conferences and the rooms in which tomorrow’s standards are written.
Artificial intelligence doesn’t create knowledge out of nothing; it derives its capabilities from extraordinary quantities of human-produced language, images, code, research and other information, because generations of human beings built the intellectual world it now learns from.
Which brings me to what may be neoliberalism’s most audacious achievement. The final victory of neoliberalism was not privatising water, railways or energy. It was convincing us that collective intelligence itself should become private property.
No individual created language, no company invented mathematics, and no corporation produced science on its own. Every generation inherits knowledge built up through countless cultures, civilisations, workers, writers, artists, engineers, teachers, researchers and communities.
That does not mean every individual work is ownerless, nor that creators should surrender their rights.
My point is larger. Artificial intelligence draws value from an intellectual inheritance produced socially across generations, yet access to the computational systems organising that inheritance is concentrating in remarkably few private hands.
The knowledge exists because humanity created it over centuries. The ability to process that knowledge at extraordinary scale is now being concentrated in a surprisingly small number of companies. We created the intellectual inheritance and now increasingly have to purchase computational access to it.
Call it innovation if you want. I can also recognise enclosure when I see it.
Artificial intelligence raises another enclosure question. Not whether knowledge can literally be treated like common land, but whether the accumulated intellectual resources of humanity will become increasingly accessible only through infrastructure privately controlled by organisations whose interests are not identical to the public interest.
The compute has an owner. The models have controllers. Somebody writes the conditions of access, receives the economic value, and decides who gets to inspect the system once it’s embedded in education, healthcare, policing or government, and who gets shut out of it altogether.
The issue is not whether companies should make products or earn profits from innovation. The question is whether humanity should become permanently dependent upon a handful of private landlords for access to an increasingly important layer of its own collective intelligence.
Britain’s new dependency
Britain is not standing outside this transformation. We are actively financing it.
The Government has established a Sovereign AI Unit backed by up to £500 million, launched Isambard-AI in Bristol and committed £2 billion to expanding UK compute capacity twentyfold by 2030, including up to £250 million for additional cloud capacity for the AI Research Resource.
I agree with the ambition. Britain should build technological capability, fund research, develop talent and avoid complete dependence upon infrastructure controlled elsewhere.
But what does the public receive in return?
Does public investment build lasting public capacity, or simply subsidise another commercial market? Will the systems it funds be inspectable, and will communities hosting the physical infrastructure see a meaningful share of the benefit? Or will taxpayers spend the next generation renting back what they helped pay to build?
The UK Government signed a memorandum of understanding with OpenAI in July 2025 covering public-sector adoption, infrastructure and technical cooperation. It explicitly identifies potential collaboration in government, justice, defence and security, education technology and AI infrastructure. The memorandum is voluntary and does not itself constitute a procurement contract, but it tells us something about the direction of travel.
This is where Sam Altman enters my thinking. His public style differs considerably from Elon Musk’s, but style should not distract us from structure. When a private company develops infrastructure that governments increasingly consider strategically important, it stops being merely another supplier.
If governments begin reorganising important public functions around systems owned by a private company, the relationship stops looking like an ordinary customer buying an ordinary product. Dependency itself creates leverage.
Britain has seen versions of this story before in water, rail, energy, outsourcing and private finance.
Public capacity weakens. The cycle continues. Eventually the cost of reversing the arrangement becomes the argument for keeping it.
Artificial intelligence may become the most consequential version of this pattern, because this time what’s being mediated is judgement, not a service.
Extraction was the first stage; governance is the second. Colonialism extracted land, industrialisation extracted labour, the digital economy extracted data — and artificial intelligence now risks extracting judgement itself. It isn’t a series of separate problems so much as the same structure repeating: one population owns the infrastructure, another is governed through it.
Oligarchy with a user interface
Elon Musk makes the democratic problem easier to see because his interventions are corrosive. Ownership of X gives him something qualitatively different from the speech of an ordinary citizen. He does not simply express an opinion on a platform; he owns the platform through which that opinion and millions of others travel.
Musk has repeatedly intervened in British politics, from publicly declaring in January 2025 that Nigel Farage should be replaced as Reform UK’s leader, to a June 2026 exchange in which then-Prime Minister Keir Starmer publicly told him to stop interfering in British politics. I do not accept that ownership and ordinary speech are equivalent simply because both eventually appear as words on a screen. A citizen can publish an opinion; a platform owner controls part of the infrastructure through which opinions are distributed.
That difference is political power, not technological brilliance. Building rockets doesn’t teach constitutional democracy, and wealth was never a democratic mandate.
Britain has its own version of the same contradiction in Nigel Farage, who presents himself as the outsider taking on the establishment while Reform UK accepted £9.94 million in donations and public funds in the first quarter of 2026 alone — more than any other British party, most of it from a small number of extremely wealthy donors.
Farage and Reform repeatedly place migration and asylum at the centre of their explanation of British decline, most recently proposing a military-led operation in the Channel in August 2026 to intercept boats carrying asylum seekers and return them to France. What interests me is the contradiction: a movement funded by extraordinary private wealth telling economically abandoned communities that people arriving with very little are the threat to their future.
Housing did not become unaffordable because a refugee exists — that has a history involving policy, taxation, wages, investment and the distribution of economic power. The billionaire class benefits when ordinary people distrust one another more than they distrust concentrated wealth.
That is why culture wars have to be understood materially. They transform failures of housing, wages and public infrastructure into conflicts about whichever minority has become politically useful that week. The anger is often real. The target is still manufactured.
The extraction of judgement
Artificial intelligence enters this political landscape as an amplifier. Governments can process more people, employers can filter more applicants, and banks can score more customers, all at a speed no human caseworker could match. Police forces can monitor more communities; militaries can analyse potential targets faster than any officer could review them by hand.
The presence of a machine can also make a political decision appear less political because institutional judgement has been translated into numbers.
Britain is already investing heavily in AI policing. In June 2026, the Government launched PoliceAI, a national centre backed by £75 million over three years, as part of a £140 million investment in policing AI that also includes funding for 40 additional live facial-recognition units.
Separately, the Government has invested an initial £4 million in developing an AI-supported crime map intended to detect, track and predict places where offences such as knife crime and antisocial behaviour may occur, using information from police, councils and social services.
There are legitimate arguments for using modern technology in policing. Police should be capable of finding dangerous people and allocating limited resources intelligently. Independent testing, public registers and clear legal standards all matter. None of that changes the history the systems inherit.
In the year ending March 2025, police in England and Wales conducted 528,582 stop and searches. Black people were searched at 3.8 times the rate of White people, while 66.8 per cent of searches across the relevant legislation resulted in no further action.
So what happens when predictive systems learn from institutional records already shaped by disproportionality?
A young Black man in London is stopped and searched. The encounter is logged. That record feeds a system that predicts where crime is likely to happen next, and the prediction sends more officers back to the same streets. The machine has not discovered a pattern. It has ratified one.
If a community has been policed more heavily, it will naturally generate more police encounters and therefore more recorded police information. Feed those records into another system without understanding how they were produced and the system can begin treating the consequences of historic policing as evidence that more policing is required.
It becomes a closed circle wearing the clothes of mathematics.
Artificial intelligence doesn’t need an explicitly racist instruction to reproduce racial inequality: it only needs to treat the historical record of an unequal institution as an objective description of society. The same problem extends into employment, healthcare, welfare, finance, immigration and border control.
Whether someone receives an interview, additional scrutiny, a loan, welfare support, a visa or police attention may be influenced by systems that classify, rank and predict.
My concern is not merely that machines can be wrong. Human beings have always been wrong. The deeper problem is that responsibility becomes harder to locate once judgement is distributed across institutions and proprietary systems.
The developer says the institution supplied the data; the institution says it relied on the software; the politician calls the matter operational; the supplier points back to whoever deployed the product in the first place. By the time you’ve followed the whole chain, everybody was involved and somehow nobody made the decision.
The environmental contradiction
None of this removes the environmental problem with artificial intelligence.
AI has a body. It lives inside data centres, semiconductor fabrication plants, cooling systems, electricity grids, fibre networks, mines and water systems. The International Energy Agency projects that global electricity consumption by data centres could more than double to around 950 terawatt-hours by 2030, slightly more than Japan consumes today.
Research from the London School of Economics warns of a growing energy, water and mineral footprint, and stresses that the eventual impact depends heavily on political decisions about where systems are built, how they are powered and what they are used for.
Communities supplying critical minerals or absorbing the environmental effects of extraction do not automatically receive an equivalent share of the technology’s economic value. The resource leaves; the cost remains.
So I reject an environmental politics that begins with telling ordinary people not to use artificial intelligence and somehow ends before reaching the boardrooms, investors, electricity providers, data-centre developers and governments. Follow the power.
An AI system helping an oil company discover new reserves might be technologically impressive while worsening climate breakdown. A system helping smallholder farmers identify disease, conserve water or reduce food waste may consume energy while producing substantial social value.
There is no machine capable of deciding whether that trade-off is just. That remains political.
When classification becomes killing
These questions become matters of life and death when artificial intelligence enters warfare.
Reported Israeli systems including Gospel, Lavender and Where’s Daddy? have been examined in relation to target generation and surveillance in Gaza. A 2025 academic analysis describes Gospel as a system for generating geographical targets, Lavender as reportedly identifying individuals, and Where’s Daddy? as reportedly tracking selected individuals until they enter family homes. The same analysis stresses that the operational details remain largely classified and relies significantly on investigative reporting and testimony from military sources.
That uncertainty is an argument for scrutiny, not for looking away; it is certainly not an argument for the mythology of automated precision that surrounds these systems.
Whatever technology is used, moral and legal responsibility cannot be transferred to software. A machine can process information faster without improving judgement, generate more possible targets without making distinction any more careful, and accelerate the decision cycle while shrinking the time left for doubt.
Artificial intelligence doesn’t make violence neutral; it gives violence processing capacity.
This matters particularly in Gaza because the UN Independent International Commission of Inquiry concluded in September 2025 that Israeli authorities and security forces had committed and were continuing to commit genocide against Palestinians in Gaza. The Commission reaffirmed in 2026 that it considered genocidal acts to be continuing.
The use of AI-assisted targeting amid allegations and findings of such gravity should make questions of human responsibility more urgent, not less.
Otherwise the same institutional disappearing act returns. Intelligence points towards the model, the model becomes part of the command process, the technology supplier identifies the limits of its contractual responsibility and somewhere at the end of all those technical explanations is a human being underneath a weapon.
The apartheid is already here
I began thinking about technological apartheid as a warning about where we might be going. I no longer think the future tense is adequate.
The structure is already visible wherever ownership of technological infrastructure sits with one group while another increasingly encounters that technology as something deciding, predicting, filtering or watching them. You can see pieces of it in policing data, borders, warfare and the relationship between the Global South and the technological economy.
The algorithm is simply the latest border.
None of this began with artificial intelligence. Empire, capitalism, racism and concentrated ownership built the foundations long before anybody started talking about large language models.
What worries me about artificial intelligence is not the science-fiction fear that the machine becomes human. It is the quieter possibility that human institutions stop questioning themselves once the machine has produced an answer.
Automating injustice doesn’t make it objective. It makes it faster, less visible and harder to challenge.
That is also why I reject the argument that racialised communities should simply withdraw from this technological revolution. Police forces are not withdrawing, nor are militaries, banks, employers, border agencies, governments or multinational corporations. Refusing to understand artificial intelligence will not stop it from governing us.
We therefore need to learn it, interrogate it and build with it while demanding something far more ambitious than ethical guidelines attached to somebody else’s infrastructure: public computing capacity, democratic oversight, community control of data, models built in languages commercial markets routinely ignore, environmental accounting that follows the whole supply chain and genuine technological cooperation that allows countries in the Global South to become owners rather than permanent consumers.
None of that is worship of artificial intelligence — it is a demand that humanity’s collective intelligence remain accountable to humanity.
Perhaps that is why those apparently small comments about my posters, ChatGPT and an “AI flavour” stayed with me.
We are living through a moment in which corporations can draw upon humanity’s accumulated intellectual inheritance and transform it into privately controlled infrastructure while ordinary people are simultaneously being taught to feel ashamed for using that infrastructure to increase their own intellectual and political agency.
Artificial intelligence has quietly become part of the infrastructure through which societies decide who receives opportunity, who attracts suspicion, whose knowledge becomes valuable, who accesses institutional power and which futures can be imagined.
So I am no longer particularly interested in asking whether artificial intelligence will shape our future. Of course it will. I want to know who gets to own that future, because communities like mine have seen this story before.
We provide the land, labour, resources and knowledge that make a new economic order possible, only to discover once everything has been built that somebody else owns the machine.
I have no intention of watching that happen again.
A note on how this was written: I drafted and edited this essay with AI assistance, the same category of tool this piece is about. The incidents, the arguments and the judgements in it are mine; the sentences went through many rounds of AI-assisted editing to get them right. If that changes how you read what’s above, I would ask the same question I have been asking throughout: not whether I used the tool, but whether I meant what I wrote. I did.
References
British colonial Kenya
Tabitha Kanogo, Squatters and the Roots of Mau Mau, 1905–63. The study documents the alienation of about seven million acres, including fertile land in what became the White Highlands, and the use of land and taxation policies to push Africans into settler labour markets.
Anaïs Angelo, Power and the Presidency in Kenya. The historical introduction describes the railway as facilitating British colonial expansion, European settlement and commerce.
Global extraction and connectivity
US Geological Survey, Congo (Kinshasa): 2024 cobalt and tantalum production and reserve data.
International Telecommunication Union, Facts and Figures 2025: global connectivity and the 2.2 billion people remaining offline.
UK artificial intelligence policy
UK Department for Science, Innovation and Technology, AI Opportunities Action Plan: One Year On, January 2026: Sovereign AI Unit, Isambard-AI, public compute expansion and cloud capacity.
UK Government and OpenAI, Memorandum of Understanding between UK and OpenAI on AI Opportunities, July 2025.
UK Government, Report on Copyright and Artificial Intelligence, March 2026.
Political finance, oligarchy and the far right
Electoral Commission, Political parties accept £24.7m in donations in Q1 2026: Reform UK’s £9.936 million total, including £9.262 million in private donations.
Reuters, coverage of Elon Musk’s January 2025 and June 2026 interventions in British politics, and of Reform UK’s “Operation Fortress” Channel proposal, August 2026.
Policing and predictive technology
Home Office, PoliceAI to speed up investigations and fight crime, June 2026: £75 million PoliceAI centre within a £140 million AI-policing package and funding for 40 additional live facial-recognition units.
UK Government, AI to help police catch criminals before they strike, August 2025: £4 million initial investment in AI-supported crime mapping.
Home Office, Police powers and procedures: Stop and search, arrests and mental health detentions, England and Wales, year ending 31 March 2025.
Environment
International Energy Agency, Energy and AI: projected global data-centre electricity consumption of around 950 TWh by 2030.
London School of Economics / Grantham Research Institute material on AI’s direct climate and environmental risks, including energy, water, minerals and infrastructure.
Gaza, artificial intelligence and international law
Iva Ramuš Cvetkovič, If Machines Could Speak: What can the AI-Powered Technologies Used in Gaza Tell us About Israel’s Genocidal Intent?, 2025, discussing Gospel, Lavender and Where’s Daddy? and the evidential questions around their reported use.
UN Independent International Commission of Inquiry on the Occupied Palestinian Territory, Legal analysis of the conduct of Israel in Gaza pursuant to the Genocide Convention, September 2025.
UN Commission of Inquiry, June 2026 update concerning Palestinian children and its continuing genocide findings.
AI authorship and the Sudan motion
Green Party motion forum comment describing the Sudanese-led motion as having a “quite strong AI flavour”.