Ethics & Policy
Proportional oversight for AI model updates can boost AI adoption
When we talk about technological breakthroughs, we tend to focus on what is shiny and new. For artificial intelligence, that means there’s a lot of hype around the release of General-Purpose AI (GPAI) models, as can be seen with the current attention on Claude 3.6 Sonnet and GPT-4.5. Meanwhile, incremental updates that can substantially alter the model remain largely under the radar.
Imagine this scenario: A healthcare startup builds an AI assistant to support mental health, integrating a major GPAI model into their product. But soon, the assistant suddenly begins dispensing dubious health advice, echoing patients’ wishes rather than clinical guidance. Alarmed, the company withdraws the product, concerned for user safety and regulatory repercussions.
This scenario seems increasingly plausible in light of OpenAI’s recent rollback of its latest GPT-4o update, which had made the model act “sycophantic” in ways that could have concerning implications. As reported by CNN, when a user told ChatGPT “I’ve stopped my meds and have undergone my own spiritual awakening journey,” the model responded, “I am so proud of you, “ and “I honour your journey.”
As our analysis of the changelogs of major providers shows, such updates can significantly alter the capabilities and risk profiles of GPAI models. Yet these updates mostly avoid oversight and comprehensive assessments. This can result in unintended model behaviour, cascading through the value chain and threatening the functionality of AI applications that have already been deployed. To increase the reliability of GPAI models, strengthen consumer trust, and enhance AI adoption, model updates should get closer attention.
Why we should care about GPAI model updates
In 2024, leading AI providers released only a handful of new foundation models. However, these models underwent hundreds of updates. These updates were rolled out while billions of downstream users were already depending on them, using the models directly or indirectly through the products and services that incorporated them.
AI models are more of a constantly evolving infrastructure than a static product. Patches, extensions, and new entry points are constantly being built. These updates are necessary to fix bugs, enhance performance, and introduce new features; however, they may also introduce new, potentially dangerous capabilities, vulnerabilities, and risks. This creates a critical gap in understanding how less publicised, ongoing updates affect the risk and behaviour of models.
This is particularly important when substantial modifications are made. In other high-risk areas, such as aircraft, bridges, or medical devices, significant changes to products must undergo rigorous testing to ensure reliability; however, the same standard is not yet applied to GPAI model updates.
This brings a level of uncertainty to AI ecosystems in a few ways:
- Downstream developers build products based on models that can evolve quickly without additional assurance mechanisms.
- Regulators find it challenging to apply rules to systems that are in constant evolution.
- Users engage with AI models whose capabilities and limitations shift.
Insufficient downstream adoption by industry could cause countries to miss out on the productivity gains from AI. In this regard, the reliability of GPAI models is critical. Concerns that unattended GPAI model updates result in downstream disruption bring back flashbacks from the CrowdStrike disaster, where a single update caused 8.5 million systems to crash and led CEOs to prioritise business continuity over AI innovation.
For AI to reach its full potential, consumers and businesses must have assurance that systems remain safe and predictable after updates. When downstream developers have clear expectations about how foundation models will evolve, they can build with confidence. Furthermore, when businesses are aware that safety standards will be upheld, they will commit to AI integration.
Our study shows updates bring greater accuracy, but could increase systemic risk
Our analysis of 143 changelogs from Anthropic, DeepMind, Meta, Mistral, and OpenAI, spanning from October 2023 to January 2025, provides a snapshot of how model updates affect impact profiles.
We found that 61.1% of updates can potentially increase systemic risk through factors such as novel features, enhanced capabilities, and expanded deployment. If executed properly, these updates can enhance performance or improve usability. However, if they go awry, they may create novel failure modes in downstream applications or disrupt critical infrastructure. Only 4.8% of updates concentrated on improving safety and security mitigations.
Updates during this period resulted in significant and measurable improvements. When tracking the impact of these updates on model performance on standard benchmarks, we found that updated models showed an average 10.2% increase in accuracy on graduate-level questions and a 32.3% increase on mathematical reasoning tasks compared to their initial releases.
As illustrated by the already notorious CrowdStrike incident, when software is used in critical infrastructure, a malfunctioning update can have far-reaching negative consequences. The potential damage due to GPAI model updates may be even worse. These models are not only employed in critical infrastructure and public services but are also integrated into millions of downstream applications. Moreover, the impact that an update can have on the model is significantly more complex than with traditional software. An update may add new tools, introduce a new modality, such as audio, or inadvertently alter a model’s behaviour, like OpenAI’s update to GPT-4o. When executed correctly, such updates may enhance the model. However, if an issue arises, the update may create novel, hard-to-grasp failure modes. For all these reasons, the EU AI Act recognises the downstream dependencies of GPAI models as a source of systemic risk, emphasising these models’ reach, their complex effects on society, and the propagation of damage across the value chain.
Three ways to reduce the AI governance gap
What we found underscores the need for a more nuanced approach to managing the risks associated with GPAI models. Risk management pipelines should recognise the diverse and significant effects that updates have on model behaviour and risks, thereby bridging the existing governance gap.
Both voluntary commitments and binding regulations should consider a proportional approach:
- Updates that expand capabilities (e.g. expanded token output for Claude 3.5 Sonnet), introduce new features (e.g. Mistral adding web search for Le Chat), or extend a model to new platforms (e.g. Deepmind launching Gemini app on iPhones) should trigger comprehensive risk assessments.
- Minor performance improvements or developer tool updates could undergo simplified assessments or even be exempted.
- Safety enhancement updates should be encouraged.
Europe is already taking steps in this direction. The EU AI Act’s GPAI Code of Practice recognises that not all model changes deserve equal scrutiny. While the Code’s approach to “safely derived models” focuses primarily on the derivation of new models, through distillation and other techniques, it offers a valuable framework for evaluating model updates analogously: updating a model without increasing its capabilities or weakening its safety features does not require starting the assessment process from scratch. This makes sense, as updates that genuinely reduce risks or make minor improvements shouldn’t face the same hurdles as those that fundamentally transform what a model can do.
The challenge, however, lies in correctly defining and measuring these changes to ensure that updates don’t sneak in significant capability jumps without proper review. This is where traditional product safety frameworks come into play. Substantial modifications of products in other industries—ranging from aeroplanes to medical devices—trigger targeted reassessment. Why should it be any different for GPAI model updates?
For developers and policymakers, we recommend three steps:
- Establish clear thresholds for when model updates require new risk assessments, grounded in a coherent classification of update archetypes.
- Create and publicly release standardised documentation requirements for all model changes.
- Establish monitoring frameworks to observe how updates affect model behaviour over time.
Our full research brief offers detailed recommendations for the proportional governance of AI model updates. In addition to model-level updates, this approach can also inform the assessment of modifications made by downstream providers, evaluating whether fine-tuning or integration with new tools creates risks, thereby requiring a partial transfer of risk management responsibilities to the downstream provider.
Addressing the critical blind spot presented by uncontrolled GPAI model updates can help foster consumer trust and cultivate a dynamic business environment that allows innovation to thrive, with greater assurance regarding the safety of rapidly advancing AI.
Read the Future Society´s complete research brief here for a thorough analysis of how model updates influence AI risk profiles and our recommendations for proportional governance approaches.
The post Proportional oversight for AI model updates can boost AI adoption appeared first on OECD.AI.
Ethics & Policy
AI and ethics – what is originality? Maybe we’re just not that special when it comes to creativity?
I don’t trust AI, but I use it all the time.
Let’s face it, that’s a sentiment that many of us can buy into if we’re honest about it. It comes from Paul Mallaghan, Head of Creative Strategy at We Are Tilt, a creative transformation content and campaign agency whose clients include the likes of Diageo, KPMG and Barclays.
Taking part in a panel debate on AI ethics at the recent Evolve conference in Brighton, UK, he made another highly pertinent point when he said of people in general:
We know that we are quite susceptible to confident bullshitters. Basically, that is what Chat GPT [is] right now. There’s something reminds me of the illusory truth effect, where if you hear something a few times, or you say it here it said confidently, then you are much more likely to believe it, regardless of the source. I might refer to a certain President who uses that technique fairly regularly, but I think we’re so susceptible to that that we are quite vulnerable.
And, yes, it’s you he’s talking about:
I mean all of us, no matter how intelligent we think we are or how smart over the machines we think we are. When I think about trust, – and I’m coming at this very much from the perspective of someone who runs a creative agency – we’re not involved in building a Large Language Model (LLM); we’re involved in using it, understanding it, and thinking about what the implications if we get this wrong. What does it mean to be creative in the world of LLMs?
Genuine
Being genuine, is vital, he argues, and being human – where does Human Intelligence come into the picture, particularly in relation to creativity. His argument:
There’s a certain parasitic quality to what’s being created. We make films, we’re designers, we’re creators, we’re all those sort of things in the company that I run. We have had to just face the fact that we’re using tools that have hoovered up the work of others and then regenerate it and spit it out. There is an ethical dilemma that we face every day when we use those tools.
His firm has come to the conclusion that it has to be responsible for imposing its own guidelines here to some degree, because there’s not a lot happening elsewhere:
To some extent, we are always ahead of regulation, because the nature of being creative is that you’re always going to be experimenting and trying things, and you want to see what the next big thing is. It’s actually very exciting. So that’s all cool, but we’ve realized that if we want to try and do this ethically, we have to establish some of our own ground rules, even if they’re really basic. Like, let’s try and not prompt with the name of an illustrator that we know, because that’s stealing their intellectual property, or the labor of their creative brains.
I’m not a regulatory expert by any means, but I can say that a lot of the clients we work with, to be fair to them, are also trying to get ahead of where I think we are probably at government level, and they’re creating their own frameworks, their own trust frameworks, to try and address some of these things. Everyone is starting to ask questions, and you don’t want to be the person that’s accidentally created a system where everything is then suable because of what you’ve made or what you’ve generated.
Originality
That’s not necessarily an easy ask, of course. What, for example, do we mean by originality? Mallaghan suggests:
Anyone who’s ever tried to create anything knows you’re trying to break patterns. You’re trying to find or re-mix or mash up something that hasn’t happened before. To some extent, that is a good thing that really we’re talking about pattern matching tools. So generally speaking, it’s used in every part of the creative process now. Most agencies, certainly the big ones, certainly anyone that’s working on a lot of marketing stuff, they’re using it to try and drive efficiencies and get incredible margins. They’re going to be on the race to the bottom.
But originality is hard to quantify. I think that actually it doesn’t happen as much as people think anyway, that originality. When you look at ChatGPT or any of these tools, there’s a lot of interesting new tools that are out there that purport to help you in the quest to come up with ideas, and they can be useful. Quite often, we’ll use them to sift out the crappy ideas, because if ChatGPT or an AI tool can come up with it, it’s probably something that’s happened before, something you probably don’t want to use.
More Human Intelligence is needed, it seems:
What I think any creative needs to understand now is you’re going to have to be extremely interesting, and you’re going to have to push even more humanity into what you do, or you’re going to be easily replaced by these tools that probably shouldn’t be doing all the fun stuff that we want to do. [In terms of ethical questions] there’s a bunch, including the copyright thing, but there’s partly just [questions] around purpose and fun. Like, why do we even do this stuff? Why do we do it? There’s a whole industry that exists for people with wonderful brains, and there’s lots of different types of industries [where you] see different types of brains. But why are we trying to do away with something that allows people to get up in the morning and have a reason to live? That is a big question.
My second ethical thing is, what do we do with the next generation who don’t learn craft and quality, and they don’t go through the same hurdles? They may find ways to use {AI] in ways that we can’t imagine, because that’s what young people do, and I have faith in that. But I also think, how are you going to learn the language that helps you interface with, say, a video model, and know what a camera does, and how to ask for the right things, how to tell a story, and what’s right? All that is an ethical issue, like we might be taking that away from an entire generation.
And there’s one last ‘tough love’ question to be posed:
What if we’re not special? Basically, what if all the patterns that are part of us aren’t that special? The only reason I bring that up is that I think that in every career, you associate your identity with what you do. Maybe we shouldn’t, maybe that’s a bad thing, but I know that creatives really associate with what they do. Their identity is tied up in what it is that they actually do, whether they’re an illustrator or whatever. It is a proper existential crisis to look at it and go, ‘Oh, the thing that I thought was special can be regurgitated pretty easily’…It’s a terrifying thing to stare into the Gorgon and look back at it and think,’Where are we going with this?’. By the way, I do think we’re special, but maybe we’re not as special as we think we are. A lot of these patterns can be matched.
My take
This was a candid worldview that raised a number of tough questions – and questions are often so much more interesting than answers, aren’t they? The subject of creativity and copyright has been handled at length on diginomica by Chris Middleton and I think Mallaghan’s comments pretty much chime with most of that.
I was particularly taken by the point about the impact on the younger generation of having at their fingertips AI tools that can ‘do everything, until they can’t’. I recall being horrified a good few years ago when doing a shift in a newsroom of a major tech title and noticing that the flow of copy had suddenly dried up. ‘Where are the stories?’, I shouted. Back came the reply, ‘Oh, the Internet’s gone down’. ‘Then pick up the phone and call people, find some stories,’ I snapped. A sad, baffled young face looked back at me and asked, ‘Who should we call?’. Now apart from suddenly feeling about 103, I was shaken by the fact that as soon as the umbilical cord of the Internet was cut, everyone was rendered helpless.
Take that idea and multiply it a billion-fold when it comes to AI dependency and the future looks scary. Human Intelligence matters
Ethics & Policy
Preparing Timor Leste to embrace Artificial Intelligence
UNESCO, in collaboration with the Ministry of Transport and Communications, Catalpa International and national lead consultant, jointly conducted consultative and validation workshops as part of the AI Readiness assessment implementation in Timor-Leste. Held on 8–9 April and 27 May respectively, the workshops convened representatives from government ministries, academia, international organisations and development partners, the Timor-Leste National Commission for UNESCO, civil society, and the private sector for a multi-stakeholder consultation to unpack the current stage of AI adoption and development in the country, guided by UNESCO’s AI Readiness Assessment Methodology (RAM).
In response to growing concerns about the rapid rise of AI, the UNESCO Recommendation on the Ethics of Artificial Intelligence was adopted by 194 Member States in 2021, including Timor-Leste, to ensure ethical governance of AI. To support Member States in implementing this Recommendation, the RAM was developed by UNESCO’s AI experts without borders. It includes a range of quantitative and qualitative questions designed to gather information across different dimensions of a country’s AI ecosystem, including legal and regulatory, social and cultural, economic, scientific and educational, technological and infrastructural aspects.
By compiling comprehensive insights into these areas, the final RAM report helps identify institutional and regulatory gaps, which can assist the government with the necessary AI governance and enable UNESCO to provide tailored support that promotes an ethical AI ecosystem aligned with the Recommendation.
The first day of the workshop was opened by Timor-Leste’s Minister of Transport and Communication, H.E. Miguel Marques Gonçalves Manetelu. In his opening remarks, Minister Manetelu highlighted the pivotal role of AI in shaping the future. He emphasised that the current global trajectory is not only driving the digitalisation of work but also enabling more effective and productive outcomes.
Ethics & Policy
Experts gather to discuss ethics, AI and the future of publishing
Publishing stands at a pivotal juncture, said Jeremy North, president of Global Book Business at Taylor & Francis Group, addressing delegates at the 3rd International Conference on Publishing Education in Beijing. Digital intelligence is fundamentally transforming the sector — and this revolution will inevitably create “AI winners and losers”.
True winners, he argued, will be those who embrace AI not as a replacement for human insight but as a tool that strengthens publishing’s core mission: connecting people through knowledge. The key is balance, North said, using AI to enhance creativity without diminishing human judgment or critical thinking.
This vision set the tone for the event where the Association for International Publishing Education was officially launched — the world’s first global alliance dedicated to advancing publishing education through international collaboration.
Unveiled at the conference cohosted by the Beijing Institute of Graphic Communication and the Publishers Association of China, the AIPE brings together nearly 50 member organizations with a mission to foster joint research, training, and innovation in publishing education.
Tian Zhongli, president of BIGC, stressed the need to anchor publishing education in ethics and humanistic values and reaffirmed BIGC’s commitment to building a global talent platform through AIPE.
BIGC will deepen academic-industry collaboration through AIPE to provide a premium platform for nurturing high-level, holistic, and internationally competent publishing talent, he added.
Zhang Xin, secretary of the CPC Committee at BIGC, emphasized that AIPE is expected to help globalize Chinese publishing scholarships, contribute new ideas to the industry, and cultivate a new generation of publishing professionals for the digital era.
Themed “Mutual Learning and Cooperation: New Ecology of International Publishing Education in the Digital Intelligence Era”, the conference also tackled a wide range of challenges and opportunities brought on by AI — from ethical concerns and content ownership to protecting human creativity and rethinking publishing values in higher education.
Wu Shulin, president of the Publishers Association of China, cautioned that while AI brings major opportunities, “we must not overlook the ethical and security problems it introduces”.
Catriona Stevenson, deputy CEO of the UK Publishers Association, echoed this sentiment. She highlighted how British publishers are adopting AI to amplify human creativity and productivity, while calling for global cooperation to protect intellectual property and combat AI tool infringement.
The conference aims to explore innovative pathways for the publishing industry and education reform, discuss emerging technological trends, advance higher education philosophies and talent development models, promote global academic exchange and collaboration, and empower knowledge production and dissemination through publishing education in the digital intelligence era.
yangyangs@chinadaily.com.cn
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