Sam Altman (seated on top right end), CEO of OpenAI, arrives to speak at a UN Security Council meeting on Artificial Intelligence during the 81st United Nations General Assembly, United Nations Headquarters, NY, September 23, 2026. (Photo by Anthony Behar/Sipa USA)(Sipa via AP Images)

What Non-Western Governments Said About AI at the UN

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Australia Wants an Off Switch for 'The Algorithm.' That's the Wrong Fix

CEO of OpenAI Sam Altman speaks during a United Nations Security Council meeting on artificial intelligence and international security under “Maintenance of International Peace and Security” on September 23, 2026 in New York City. Tech industry leaders including the CEOs of OpenAI, Anthropic and Hugging Face briefed ambassadors on the risks of artificial intelligence. (Photo by Alexi J. Rosenfeld/Getty Images)

At the UN and in Washington, Leaders Clash on Approach to AI

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Supporters of presidential candidate Flavio Bolsonaro with a President Trump inflatable, holding a doll of President Lula in a prison unifom, during an Independence Day rally in Sao Paulo, Brazil, Monday, Sept. 7, 2026. (AP Photo/Andre Penner)

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US Leadership on Open-Weight Models is Key to Global AI Safety 

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European Commissioner for Tech Sovereignty, Security and Democracy Henna Virkkunen. (AP Photo/Marius Burgelman)

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UN Secretary General Antonio Guterres holds a press conference at the UN headquarters in New York on Sept. 16, 2026, at which he spoke “about rising concerns regarding the advance of artificial intelligence.” (Kyodo via AP Images)

UN Secretary General Antonio Guterres holds a 

 at the UN headquarters in New York on Sept. 16, 2026, at which he spoke “about rising concerns regarding the advance of artificial intelligence.” (Kyodo via AP Images)

Who Is AI Risk For? Centering People in the AI Risk Conversation

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 debate how to regulate and slow the development of frontier AI, countries across the rest of the world are approaching the question differently.

United Nations General Assembly High-Level Week

 last week, governments called for a greater role in shaping AI rules, expanding access to AI capacity and ensuring that efforts to make AI safer do not deepen existing technological divides. Pakistan, for example, backed calls to pace frontier AI while warning that “pacing must not become a new form of gatekeeping.”

Here is how governments outside the Western AI powers and China approached AI governance during UNGA.

A massive ship loaded with several dozen yellow trucks is docked by a port. A cement truck is lifted above the ground by a loading crane. The name of the ship is Chinafrie Happiness. This is written with large white letters on the side of the ship. 

The US-China AI race has once again emerged as a key area of focus in global news headlines, particularly following the conclusion of the recent 

of the global AI performance gap between the two countries earlier this year. This is not to say that the two countries are at parity in every dimension of AI performance; they have 

 in areas such as patent volume, model performance, research output, and investment. Nonetheless, this development signals progress toward 

China’s AI development efforts are unfolding alongside its expanding economic influence on the African continent. Over the past two decades, China has 

 from an emerging donor in Africa to the continent’s largest bilateral creditor. Its strategic position on the continent is creating opportunities for its economic partnerships to intersect with its broader global AI ambitions.

To this end, China has deepened and leveraged its economic relationship with African countries in various ways, from educational programs to political exchanges. Of these, however, critical minerals, foreign direct investment in digital infrastructure, and market penetration for Chinese AI models stand out as among the most important.

, such as data centers, advanced chips, batteries, and other electronic components, is rising. In turn, so is demand for critical minerals such as copper and germanium, which are used in electrical infrastructure and semiconductor manufacturing.

Africa possesses abundant deposits of these minerals. Namibia, for example, possesses germanium deposits, while the Democratic Republic of the Congo (DRC) holds 

 for the hardware that underpins the AI boom, especially 

China positioned itself as a key player in the DRC’s mineral supply chain as 

, which granted Chinese state-owned enterprises access to the DRC’s vast copper and cobalt mines in exchange for infrastructure funding commitments. This enabled Chinese firms to acquire 

 in the DRC’s copper mining operation. In 2025, DRC copper exports increased by 10 percent, with Chinese firms playing a 

China also currently holds a dominant position in the global germanium supply chain. In 2025, a Chinese firm, Sinomine, 

 to construct a germanium plant in Tsumeb, Namibia. This mineral has 

 in AI infrastructure, such as specialized semiconductors and high-performance fiber optics, a boon for China’s AI supply chain.

 of infrastructure projects in sub-Saharan Africa, especially in sectors such as transport, energy, and mining. This has placed African countries in a position to receive China’s 

 of infrastructure funding, distinguished by a willingness to provide 

 with varying degrees of concessionality and occasional debt relief.

Poor infrastructural readiness is a major constraint on AI development in Africa, as many countries on the continent are 

 in critical domains such as reliable power for network connectivity and secure cloud architecture for compute. 

 of the need to secure Africa’s AI future by ensuring reliable digital connectivity and energy access has created an opportunity for China, which is already 

 of Africa’s infrastructure revolution, to further expand its influence on the continent.

This growing influence has enabled it to advance its 

, which aims to create global connectivity through new trade routes, and especially to provide it with a 

 by diversifying transportation routes that reduce exposure to disruptions along concentrated global supply chains. In turn, China’s 

 which is part of the BRI, is playing a significant role in Africa’s AI infrastructure readiness by financing 

 on the continent with the help of private multinational companies and policy banks.

The growing African market for Chinese technology

Chinese AI models have become a key building block for tech innovation in African countries, evidenced by their 

 among developers in Africa, especially Uganda, Kenya, and Nigeria. Because such developing countries face compute, connectivity, and data 

, developers have found innovation to be more accessible where open-weight models are locally modified as opposed to building models from scratch.

It is within this ecosystem that China’s open-source AI models like Qwen and DeepSeek have become 

 for their relatively low-cost deployment, open-weight access, and fine-tuning flexibility. While these AI models practically appeal to African developers, the growing use of Chinese digital technologies is giving Chinese firms greater access to African markets while creating opportunities for China to advance its technological interests across the continent. The more African businesses build applications on top of Chinese models and run them over Chinese servers, the more revenue accrues to Chinese AI companies and the more likely other African developers are to do the same.

, Africa, home to about 17 percent of the world’s population, already represented a large potential consumer market, with an AI market value of $4.51 billion and a compound annual growth rate of 27.42 percent. So far, Chinese technologies have gained a foothold in this market in domains such as agriculture, where Chinese-made agricultural drones support 

 are advancing medical research; and national security, where 

 are driving smart city initiatives in Africa.

While China’s economic engagements in Africa have funded substantial developments in parts of Africa, its regional partnerships have also shaped market dynamics, consumption patterns, and resource acquisition, potentially giving China strategic advantages over its primary competitor in the global AI race, the US. Taken together, these channels, which have emerged due to China’s economic position on the African continent, could undermine the efficacy of the 

, particularly its efforts to counter China’s global technological influence.

This plan identified strategies including exporting American AI to meet global demand and strengthening export controls to block China’s access to critical components in the semiconductor manufacturing pipeline. The effectiveness of these strategies depends in part on the US’s alliance-based structural power, which has contributed to its influence across these critical supply chains and has 

 by relationships with other technology powers such as the UK, Israel, the EU, and South Korea.

However, China’s advancements already pose a 

 to the US’s global structural power, and its strong economic and technological presence in African countries gives it opportunities to pursue its AI ambitions despite US efforts to hinder them. Specifically, the factors described above (access to critical minerals, infrastructure networks, and African markets for AI products) all strengthen China’s position in key areas in the AI ecosystem, thus potentially limiting the effectiveness of the US’s efforts to check its AI ambitions.

China’s engagements with African states illustrate that Africa is not simply a spectator of global AI developments but an emerging actor in the geopolitics of AI. Its demographic growth, natural resources, and expanding consumer markets have attracted Chinese investment, highlighting the continent’s strategic importance. This has created an opportunity for African states to leverage these advantages to build domestic AI capacity, promote regional cooperation, and strengthen representation of their interests on the global stage.

However, African states must also establish safeguards to prevent external investment from cultivating digital dependence. Such dependence could erode the digital sovereignty of affected states, leaving them 

 over political, social, and economic outcomes. Therefore, African countries should diversify technology partnerships and establish robust digital frameworks capable of addressing surveillance and data governance concerns. To maintain the possibility of mutually beneficial relations, African states must also prioritize regional economic integration to increase their collective bargaining power over the terms of AI investment and technological cooperation on the continent.

This month, the federal judge in Virginia who declared Google an illegal monopolist made public her ruling on remedies. As a colleague of mine recently 

 stopped well short of actually preventing future abuses of monopoly power and show the continued need for legislation. The unsealed memorandum shows that the court found that calls to break up Google’s ad-tech empire are “neither realistic nor needed” and that behavioral remedies designed to alter Google’s monopolistic behavior are all that are necessary to “pry open” competition.

Quick refresher: US Judge Leonie Brinkema 

 that Google’s digital ad-tech empire was an illegal monopoly. Google’s acquisition and consolidation of the ad-tech stack (the layers of software that broker and execute advertising buys by advertisers from publishers) into its Ad Exchange (AdX) was anti-competitive behavior. Said consolidation allowed other anti-competitive behaviors like tying, self-preferencing and price manipulation. As a result, advertisers needed to pay more for ads (even while losing visibility into whether or where they ran), and publishers needed to build higher paywalls to sustain their business.

So what are the behavioral remedies that will supposedly alter Google’s behavior away from its monopolistic past? On their face, they might seem likely to accomplish something. First, Google cannot require publishers that use its ad server, DoubleClick for Publishers (DFP), to also use AdX. Second, AdX and DFP must be integrated with Prebid, the open-source software that lets publishers invite multiple ad exchanges to bid for ad space. Third, AdX must make real-time bids available to rival publisher ad servers. Fourth, publishers get to access and export their data from AdX and DFP. Fifth, AdX and DFP can’t discriminate to favor Google’s products. And finally, a court-appointed monitor will police Google’s compliance.

As nice as these remedies sound, they don’t actually remove the incentive for Google to favor its own products; Google still owns both its ad server and exchange along with the conflicts of interest therein. We have 

 showing its commitments aren’t worth the paper they are printed on. In 2007 Google bought ad-tech company DoubleClick and promised it would not combine the web browsing data from DoubleClick with the user data from existing Google assets — 

. In 2024 French regulators fined Google for 

 made in 2022 to address competition concerns with news publishers. No wonder privacy expert Alan Chapell 

 “brilliant at playing the behavioral remedies game.”

And then there is the weakness of the compliance monitoring. On the one hand, the monitor is supposed to immediately report if Google is failing to comply with the remedies. On the other hand, Google is allotted a minimum of 30 days to respond to any concern the monitor may have about a violation (so “immediately” means after 30 days). Ad-tech executives 

 concern before the unsealing of the order that even if Google tried to bend but not break a remedy, the complaint would get dragged out in the courts for months to the point that by the time any relief was granted, the damage was done. Providing Google with a “broad right to object” (the ruling’s words, not mine) sets up the remedies for failure.

, it has become clear that existing law alone is not enough to rein in Big Tech companies even when they’re found liable for engaging in illegal behavior. This is especially frustrating as the Federal Trade Commission just filed a 

 showing exactly what a company can do when it controls multiple sides of a digital advertising ecosystem: It alleged that Amazon used hidden surcharges to extract $20 billion from ad customers.

Fortunately, there is bipartisan legislation that gets at the heart of the problem of monopolistic digital ad tech. The 

Advertising Middlemen Endangering Rigorous Internet Competition Accountability (AMERICA) Act

 prohibits companies from owning more than one part of the digital ad ecosystem if they take in over $20 billion in digital ad revenue. Under this requirement, Google could keep only one entity among its ad exchange AdX, supply-side platform DFP, or demand-side platforms Google Ads and DV360. This would eliminate the conflicts that drive Google’s digital ad monopoly.

Additionally, the bill requires companies that make over $5 billion in digital ad revenue and provide either supply- or demand-side services to act in the best interests of their customers. That would prevent Amazon from adding surcharges that, by their own admission, brought no value to customers and only served to increase its revenue.

, “We need to open up digital markets to bring about more competition and more choice if we want an advertising system that works for the rest of us.” It has become clear that we’ll need AMERICA for that to happen.

On September 8, the Australian government released an exposure draft of its proposed 

, a welcome and necessary reform requiring online services to proactively identify foreseeable risks of harm and take reasonable steps to address them. It is a positive step towards protecting children, in particular, from the worst forms of harm originating online.

The draft bill also requires platforms to provide “user empowerment tools,” which are yet to be specified. But across 

, encouraged by the government’s messaging, 

 seized on an idea far simpler than the full proposed reforms: Australians would be able, for the first time, to 

,” the government sold the duty of care as a proposal to provide safety for vulnerable users — especially children — and to give users aged 16+ greater control over whether their primary social media feed maintains the status quo of algorithmically recommended content or reverts to only displaying material from accounts they choose to follow, in chronological or reverse chronological order. More extensive, accessible and meaningful user control over the online experience is certainly needed, and the draft bill offers 

 to push platforms beyond the status quo. But why has the simplistic idea of an on-off switch for algorithmic recommendations become such a focus of the government’s public case for reform?

Part of the answer is that, in government rhetoric and public debate, ‘the algorithm’ has become a powerful shorthand for the much larger machinery of platforms’ power and how they curate and serve content to users. Harmful content recommendations, predatory advertising, addictive design, political polarization and creator visibility are all now framed as problems of ‘the algorithm’. The concerns are real, but collapsing them obscures the fact that algorithms are not ‘bad guys’ in and of themselves. What matters is how algorithms are optimized: What signals they maximize, whose interests they serve, and what effects they produce at scale.

There is no single ‘algorithm’ that can be switched on or off. Data-driven machine learning systems incorporating algorithms are the building blocks of many computational systems.

Even if algorithms could be switched off, it’s typically not advisable. Recommender systems help solve an abundance problem: there is vastly more content available than any person can navigate, including from those that we may directly and actively follow. Recommender systems help curate and locate music, films, people, products and information.

Digital platforms also use algorithms to moderate and recommend content, rank search results, target advertising, and detect spam or other inauthentic activity. They underpin safety systems and are critical to delivering a diversity of content to users. Increasingly, these processes involve machine learning models drawing on large volumes of behavioral data captured by these same platforms, which also means that user practices already partly shape the content of algorithmic feeds. In short, platforms must always decide which content is eligible to appear, what is removed or demoted, how spam is detected, where advertising goes and how other content is presented — and this requires algorithms.

The problem, then, is not the existence of algorithmic recommendation, but what these systems are 

, whose interests those choices serve and what consequences follow. Recommendation systems need not prioritize advertising dollars and eyes on screens.

For example, in a long-term experiment to optimize for kids’ and teens’ wellbeing, YouTube has worked routinely with a group of 

 to channel evidence-based guidance into detailed tweaks of their recommender systems for minors’ accounts. Algorithms already weed out egregious content that violates platform guidelines but this leaves the more delicate question of how to deal with the corpus of content which is non-violative but potentially harmful for children at volume (think, endless makeup videos for girls). YouTube has developed and applied 

a dual system of safeguards and quality principles

 to ensure that low quality content that might be harmful at volume is systematically deprioritized and dispersed through kids’ and teens’ feeds to limit how often they encounter it. Unfortunately, since the implementation of the social media age restrictions, Australian under-16s can no longer benefit from this wellbeing-oriented optimization of teen accounts, including teens parent-supervised accounts.

“My Feed, My Way” contrasts the recommended feed with a chronological and/or “following” feed. Giving users choice is sensible and many platforms already offer tools to modify, disable or clear the algorithms shaping their feeds, although they are often difficult to find, use or understand. But chronology is not the absence of algorithmic mediation. A chronological or “following” feed still applies rules about what content is included, excluded, and ordered from a potentially vast pool of material. More importantly, presenting users with a choice between two platform-defined modes offers only a limited form of agency. Our research on advertising transparency has shown the limits of the enhanced-controls 

: providing users with information and controls does not necessarily render platform systems transparent or enhance users’ agency over them.

Nor does user choice necessarily need to mean a binary choice between complex, platform-driven algorithmic recommendation (which usually includes chronology and/or followed accounts) and linear chronology or followed accounts alone. Feeds could prioritize users’ most loved accounts, allow users to nominate sources they particularly value, or provide more direct transparency and control over ranking objectives. Alternative social media platforms like Bluesky take this a step further with its concept of a “

” where users can design and share curated feeds for the benefit of the wider community.

The algorithm doesn't operate on an empty world

Treating recommendation as the source of harmful content can also obscure a more basic problem: the content must exist before it can be recommended.

This is particularly relevant in relation to Australia's social media minimum-age restrictions. Young people without accounts can still access designated platforms in a logged-out state. For a recommender, this creates a classic “cold start” problem: if the system knows almost nothing about a user, what should it show them?

When examining logged-out or new account experiences, many report on how quickly poor-quality material is surfaced. This raises questions about what platforms recommend to an unknown user, but also about the pool of content from which those recommendations are drawn. If misogynistic or violent material is readily available before a recommender knows much about a user, removing personalization does not resolve the underlying problem.

Importantly, the Digital Duty of Care itself recognizes this broader problem. Particularly for children, it reaches beyond algorithmic recommendation systems to the specific material made available by a service, the design features through which users encounter it, and the wider systems and processes that give rise to risks of harm. In this respect, the duty of care is considerably more sophisticated than the public debate about on-off switches for algorithmic feeds suggests.

The Digital Duty of Care exposes the contradiction

There is a curious tension in the government's position: presenting ‘the algorithm’ as something from which users may want protection, while requiring platforms to become better at anticipating and mitigating harms, which logically requires more sophisticated algorithmic approaches, not fewer.

At platform scale, identifying spam, abuse, coordinated harmful activity or patterns of risky negative exposure cannot be done manually. If we want platforms to identify repeated exposure to, for example, eating-disorder content and intervene, we are asking them to classify content, detect patterns and change how material is ranked or recommended for some or all users.

This is why the detail of the Digital Duty of Care is more interesting than the rhetoric surrounding it. Its central idea is that providers should identify foreseeable risks of harm and take reasonable steps to mitigate them. The bill explicitly asks providers to assess potential harms arising not only from content, but from design features and other systems and processes. It assigns to platforms an overarching responsibility to consider, monitor and address the outcomes of algorithmic features like recommender systems, requiring a ‘safety by design’ approach.

The bill’s proposed rules requiring the provision of user empowerment tools also offers and requires a kind of flexibility that is very far from an on-off switch for algorithmic feeds. Platform specificity will be important: the algorithmic features of YouTube work differently from those of TikTok, Facebook or Instagram; TikTok may have a dominant ‘For You’ feed, while Instagram distributes recommendation across its main feed, Explore and Reels. Even identifying which ‘algorithm’ a user is supposedly switching off is less straightforward than the slogan suggests. Consequently, the controls users need to gain more agency over their experience will differ across platforms.

There is another important but lesser discussed detail of this bill. Politicians can demand that platforms ‘fix the algorithm’, but governments, researchers and the public have 

 to determine what these systems actually do.

Source-code transparency is not enough. Socially consequential outcomes emerge when recommendation systems interact with different users, content, contexts and behaviors.

What we need is observability: the capacity to examine these systems in operation. What are different users encountering? Are some groups systematically exposed to different content? What specific changes to recommender systems are effective in reducing harm? Can independent researchers test a platform's claims about what its interventions have achieved?

This matters particularly under a duty of care. If platforms must identify risks of harm, mitigate them and assess whether their interventions work, independent actors require the capacity to verify those claims. Otherwise platforms remain responsible for both operating the systems and telling us whether they are safe.

Encouragingly, the bill begins to recognize this problem. Providers would be required to conduct and retain risk assessments, while separate provisions create a basis for researcher data access and protect approved researchers using ‘sock puppet’ identities to study platforms. The details of those access arrangements will matter enormously, but the principle is important: accountability cannot depend solely on what platforms choose to tell us about their own systems.

Perhaps this is ultimately the problem with ‘the algorithm’ as our regulatory target. It directs attention towards a mechanic when the more important questions concern systems: what they optimize, how they are designed, what choices they afford, what harms they produce, and whether anyone outside the companies operating them can independently see the results.

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