OECD Expert Group on AI Futures –Meeting 2 (18th & 20th September 2023)
Background
The OECD.AI Policy Observatory and OECD Strategic Foresight Unit convened the second meeting of theExpert Group on Artificial Intelligence (AI) Futures on 18 and 20 September 2023. The expert group is acore component of the OECD workstream on AI Futures.
The Expert Group is led by three co-chairs:•

Stuart Russell, Professor of Computer Science at the University of California, Berkeley and Directorof the Centre for Human-Compatible Artificial Intelligence.

•Francesca Rossi, IBM Fellow and AI Ethics Global Leader.

•Michael Schönstein, Head of General Digital Policy - Federal Chancellery of Germany.

The full composition of the Expert Group is available here. The list of participants for this second meetingcan be found in Annex A of this document.

Introduction and context
To help accommodate different time zones and enhance inclusiveness, the meeting was divided in two
sessions, each with the same agenda (18 and 20 September 2023). The meetings were held under the
Chatham House Rule. Karine Perset, head of the OECD.AI Policy Observatory, kicked off the meetings
with a general overview and context regarding the format of the double session and its contents. Ms. Perset
invited experts to join a hybrid meeting of the expert group on November, along with other OECD.AI
Network of Experts meetings of the OECD expert groups on AI Incidents and AI Risks and Accountability(see subsequently published video of the 9 November session).
The discussion of the meeting largely focused on expert group members’ thoughts on the early results ofan OECD survey about potential future AI benefits, risks and solutions. The expert group had previously
discussed the survey concept at the first Expert Group Meeting and had the opportunity to provide
feedback on the draft survey items through an asynchronous brainstorming and validation exercise, which
resulted in 131 potential future benefits, risks and solutions that subsequently comprised the survey
contents. The OECD administered the survey starting in August 2023.
In turn, Jamie Berryhill (OECD.AI Policy Observatory), Hamish Hobbs (OECD Strategic Foresight Unit)
and Alistair Nolan (OECD’s Directorate for Science, Technology and Innovation) presented insights to leadthe discussion. Mr. Berryhill presented the main results of the survey, Mr. Hobbs then further elaborated
on how the results will be used to create foresight scenarios, and finally, Mr. Nolan presented on a 2024
workshop to be held as part of the OECD programme on AI in Work, Innovation, Productivity, and Skills(AI-WIPS).
The co-chairs facilitated the discussion items listed below.

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Topic 1: Discussion of initial survey results
OECD Secretariat presentation
Jamie Berryhill, AI Policy Analyst from the OECD.AI Policy Observatory, presented the results of thesurvey. The survey items consisted of 131 future considerations (43 risks, 21 benefits, and 67 solutions)that were identified through extensive OECD research, discussions held in previous events androundtables, and through the incorporation of expert group member feedback from previous sessions anda follow-up validation exercise.
Each consideration was assessed along two axes: importance and actionability, on a scale from 0-10. A
description of how “importance” and “actionability” can be found in Box 1.
Box 1. Context for “importance” and “actionability” provided to expert group members••

Importance. In your opinion, how important is it that governments focus on this item? Your
thinking should weigh both the magnitude of potential impacts from a given risk, benefit, or
policy solution and the probability of these impacts. In assessing impacts you could, for instance,take into account the level of harm that you perceive for potential risks, the societal or economic
good that could be yielded by potential benefits, or the magnitude of positive change that could
be brought about by implementing potential solutions. Please use the following examples togive a sense of the rating scale:o

0. This is in no way important for governments and the international community.

o2.This is an issue of marginal importance for governments and the international community.

o

4. This is a somewhat important issue.

o

6. This is an important issue, but not among the most important.

o

8. This is among the most important issues for governments and the international
community.

o

10. This is the most important issue for governments and the international community.

Actionability. Assuming that political will exists, based on all of the factors that you can thinkof (e.g., feasibility, level of complexity, ease of implementation, current and perceived futuretechnical ability and financial resources, etc.), what is your opinion on how actionable the item
is in terms of the ability of a group of like-minded countries to make a significant impact withregards to mitigating potential risks, yielding potential benefits, and putting in place potential
solutions. For this item, it may be useful to ground your thinking in the medium-term (over the
next 10-20 years).
o

A rating of zero (0) implies that you think there is no meaningful way for governments tomitigate this risk, contribute to seizing this opportunity or effectively implement this solution,even through collective action.

oRatings in the middle – five (5) – imply that a group of like-minded governments could havesome agency in mitigating a risk, seizing an opportunity or effectively implementing
solutions, but that their success may be partial, uncertain or require an unusually largecommitment of resources or high level of global collaboration.

o

A rating of ten (10) implies near certainty of almost entirely mitigating the risk, realizing thebenefits, or effectively implementing solutions based solely on the actions of like-minded
governments.

Given the diversity of the group’s membership, the level of agreement and disagreement is also an
important dimension to take into account when understanding views on the survey elements. Thus, a3measure of variance (i.e., level of disagreement among members) was also calculated for each item (seeFigure 2). Identifying considerations with the highest levels of variance may, for instance, surface areas
ripe for discussion and productive debate.
Based on importance and actionability scores, items were graphically presented as belonging to one offour quadrants of a cartesian graph, as in Figure 1 below. Figure 2 illustrates how the dimension of variancecould also be visualised.

Figure 1. Responses explored two dimensions

Figure 2. Analysis also accounts for variance in experts’ responses

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Each quadrant signals the combined score of survey items along the actionability and importancedimensions. Depending on the graphical positioning, items can be considered as belonging to one of four
different categories:•“Low-hanging fruit”, later renamed “policy opportunities”, for items that have high scores for both
metrics and could perhaps be important for action with fewer barriers to achievement then thoseclassified as “dedicated attention needed”.

•“Dedicated attention needed”, renamed “heightened attention needed” for items with highimportance but with a low score on perceived actionability opportunities. Thus, these items wereperceived as important by the experts, but with more perceived barriers to achieving them thanthose discussed in the previous bullet.

•“Nice to haves”, for items with high actionability which are perceived as easily materialisingthrough policy actions, but of relatively low importance.

•“Not worth it”, later renamed “not high priority” for items that are considered to be of marginalimportance and low actionability.

However, as seen in Figure 2, even if an item is ranked in a particular quadrant, there may be significant
variance underlying the response, which should be considered in interpreting the results.
As the results of the survey presented at the expert group meeting were early and changed to account for
additional submissions after the meeting, the specific results are not presented in this summary. However,
final results, as well as a description of each topic, will be provided in two forthcoming reports:•

Exploring Artificial Intelligence Futures: Prospective milestones, benefits and benefits.

•

Exploring Artificial Intelligence Futures: Prospective solutions and governance approaches.

Expert group views on the survey and its results
In general, experts appreciated the design of the survey and the universe of options presented forconsideration. However, some members critiqued the length of the survey, which they suggested mayneed to be reduced if it were to be leveraged for a wider audience. Further thoughts advanced by expert
group members can be grouped thematically in the following points:
Clarify the meaning of low actionability when communicating results. Experts expressed their viewon how to consider items with a low rating on actionability. They suggested clarifying that that low
actionability does not signal lo priority of a consideration for a survey item, but only a perceived difficultyin taking action to realise it. The OECD Secretariat responded that will incorporate this feedback and clarify
the meaning of actionability in forthcoming reports the leverage the survey results.
Further explore disagreement among members. Experts expressed that one of the strengths of thegroup is its diversity of perspectives. Hence, they encouraged exploring areas of disagreement in order topromote fruitful discussions and to gain and fuller understanding of the context of these areas. Expertssuggested to consider exploring three approaches:•

Surveying experts again at a later date on the extent to which, if any, they have had a changeof opinion about survey items over time. The OECD Secretariat noticed this comment and willconsider its feasibility and usefulness for future efforts.

•

Following the Oxford Process working method, in which topics of scientific controversy
disagreement are debated until scholars agree on a description that is a fair representation ofdisagreement. The OECD noted this comment, and also indicated that expert group members
would receive drafts of any future publications for their review to help ensure the topics andpotential debates around them are described in a way that everyone can agree is fair.

5•Discussing areas of disagreement to help identify and characterise the source of disagreement
concerning specific considerations (e.g., variance in scores in some areas). The OECD Secretariat
noted the importance to further investigate these areas in future meetings and scenario exploration
exercises.

•

Consider the impact of AI progress’ speed. Concerning the nature of AI change, it is paramountto understand both the development speed and impact of this technology. While the survey
prominently focused on impact, it did less so on speed. However, this dimension is noteworthy as
it has the potential to influence the effectiveness of human responses based on the pace ofdevelopment. For instance, gradual technological changes would be associated with more capacity
to respond, at parity of potential predicted impact. The OECD noted that it would conduct future"super forecasting” efforts focused more on time horizons.

•

Consider the human dimension when assessing impacts. Equally important to accurately
assess impacts is the assumption on the nature of human behaviour associated with the
management of the technology. In particular, at the micro level, assuming that individuals have theability to learn and adapt would yield different impacts than a scenario in which humans are static
beings that slowly react to change. At the macro-level, this consideration translates in observations
about the institutional capacity of societies.

•

Foster a meta-analysis approach. Some experts expressed that humans tend to be biased
towards things they know, so it is reflected in initial results. AI “safety” , for instance, is something
one can grasp, so some responses in this area appeared stronger. This may also be seen in thesomewhat higher level of importance given to risks compared to benefits. This can be a limitation
for future-oriented efforts, as it is challenging to get people to think beyond concepts with which
they are already familiar. It was suggested that, in addition to the survey, the OECD could try tocreate a handful of visions of the future to help ground thinking. The OECD discussed that future
efforts would narrow the scope of focus over time and orient more around hypothetical futurescenarios to help ground thinking and discussion.

•

Consider “funding and adoption" as an isolated metric. Some experts expressed thatsustainable funding of solutions, which was a consideration under the “actionability” axis could be
a separate item of evaluation, and that adoption (e.g., by cities, businesses and communities) is
an important question that was not explicitly mentioned under the actionability item and should be
discussed.

•

Increase focus on current technical trends. One of the experts argued that the group should
spend some time assessing the new stage we have entered of “artificial capable intelligence” (ACI),
suggesting that new developments in AI in the present and near-term are underappreciated relative
to longer-term discussions.

Topic 2: Potential futures and scenarios
OECD Secretariat presentation
Based on previous discussions and the survey results, Hamish Hobbs from the OECD Strategic Foresight
Unit presented views of desirable and undesirable futures, depicting the most important and sufficiently
actionable risks and benefits (Table 1). Threads were drawn from the top 20% most important items fromthe initial survey results that were also on the top half the actionability scale.

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Table 1. Desirable and undesirable futures based on survey results
Desirable futures
Controlled training and deployment of high-risk models andapplications.
Strong technical tools for safe and ethical AI.
International cooperation to ensure safe and ethical AI.
Widely distributed AI benefits.
Controls to prevent excess power concentrations.
Empowered public with strong democratic and civil society oversight.

Undesirable futures
Lack of governance of high-risk models and applications.
Lack of technical tools for safe and ethical AI.
AI is frequently misused by malicious actors.
International competition drives a race to the bottom on AI safety andethics and prevents collaboration on shared rules.
AI benefits accrue to a select few.
AI concentrates power in harmful ways.

Mr. Hobbs explained that these envisioned futures would be used to develop scenarios for a scenario
exploration exercise for the next meeting of the Expert Group on 9 November. The scenarios will be used
to identify which policies are required to push us towards the beneficial futures and away from the harmful
futures.
Expert Group members were invited to react to the current key threads identified by the OECD Secretariat,
to better inform the scenario development.

Expert group members’ views
Experts reacted to the potential futures, further analysing the consequences that certain AI applications
might be associated with in a future society. They also suggested ways to develop the concepts into
complete scenarios. The insights can be categorised in (I) areas of convergence or agreement, (II) areasof disagreement, and (III) other insights.

Areas of convergence of experts’ opinions
Create inclusive scenarios. To better inform the general public and reach a wider audience, expertssuggested to increase contextual clarity when developing scenarios. Given the differences between
countries in terms of industrialisation level or regulatory culture, experts suggested to better define thecontextual nuances of information asymmetry or concentration of power considerations. In this regard, it
is crucial to explicitly outline the implications for society and the rule of law, when some countries are notrepresented by governance systems or when minorities are excluded from a certain policy intervention.
These considerations must hence be included to have future scenarios that are inclusive andunderstandable.
Explore origins of consensus. Several experts expressed their satisfaction to see where the consensus
has emerged to date. They suggested to keep this consensus still at a high-level, opening space rather
than diving into the specifics of AI impacts considerations, which generally tend to be more divisive. For
instance, survey's results signal particular convergence on controlling dangerous models while also
mitigating concentrations of power. Experts also seemed to agree, for instance, that a full ban on advanced
AI would be detrimental while more controlled release protocols might be beneficial. In this context, theneed for further and constant research on high consensus items has been depicted as having the potential
to bring essential insights to the group.

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Areas of divergence of experts’ opinions
Analogy between nuclear and AI technologies.
In the context of potential governance solutions to mitigate the negative impacts of AI, promotinginternational cooperation and regulation along the lines of what has been treaties as has been done with
nuclear weapons is often presented as a potentially effective approach to mitigate AI risks.
•

However, one expert noted how the comparison between AI and nuclear power is potentiallydetrimental to effective progress. According to the expert's view, the analogy breaks down very
quickly, given the invisible nature of AI harm as opposed to nuclear weapons. In particular, as AI
is woven into the fabric of society, its effects are neither physical nor perceptible, as in the case of
nuclear systems. Hence, scenarios and proposed governance responses should clarify.

•

Other experts, though, noted how radioactive radiation is also invisible, it is important to clarify
the similarities and differences between nuclear and AI technology, when drawing this analogy.
Approaches to AI regulation•

One expert suggested considering policy and governance responses whose scope isbeyond strictly AI regulation. For instance, data protection legislation may represent the mosteffective way to mitigate or manage some of the negative impacts of AI. Generally speaking,holistically addressing issues - for instance by promoting individual rights - is a promising approachthat protects human rights including in and beyond the AI sphere.

•

However, another expert suggested focusing more on fostering governments’ capacity toregulate AI specifically. According to their view, for positive futures to materialise, it isfundamental that governments have a capacity to regulate. To foster this capacity, there are four
conditions to be met. First of all, it is essential that resources be directed at developing technicalcapacity, increasing budgets, and ensuring authority. Secondly, the legal authority to impose
penalties must be ensured. Thirdly, there has to be enough international collaboration to prevent
substitution effects. Finally, an essential element is the existence of political support in communities
around the world.
AI governance and AI safety•

According to one expert, governance debates on broad societal impacts should be prioritisedover narrower considerations on AI safety. Concentrating the efforts and debates aroundgovernance keeps the conversation broader to include all options available to institutions tomanage AI deployment in society. Furthermore, as the Global South has very different governanceneeds, these should be considered.

•

On the other hand, one expert highlighted how, despite agreeing that focusing on governanceshould be the final goal, incentivising AI safety represents a fundamental step to increase andfoster the regulatory capacity of governments.

Other insights
Alternative political processes
According to one expert, in a democracy, it is essential that AI systems are developed and deployed in amanner that satisfies the majority, and not necessarily the totality, of the population.
By integrating AI in society once a general level of agreement is reached, it would be possible to create
very large gains for the general population in a fast-paced manner, to then focus on how to accommodate

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the needs of the remaining part of the population. This would represent an antithetical approach to theinefficient one characterising present decision-making structures. The expert argued that current politicalprocesses focus on solutions that are a compromise between opposed extremes of opinion, in order tosatisfy all parties in society. However, this approach results in slow implementation processes that impede
society from reaping the benefits of AI in a reactive way.
Consider uncertainty and context as key dimensions to build scenarios
One of the experts identified two varying aspects that differentiate scenarios from one another. On one
hand, different scenarios vary in terms of uncertainty, meaning that the risks and benefits that characterise
them differ in terms of materialisation likelihood. In scenarios where certain risks have already or almost
concretised, it was suggested to focus on the evolution of the said risk.
On the other hand, scenarios should take into account dimensions of context, as countries around theworld greatly differ in terms of industrialisation level or political ideology. Hence, the same scenario would
evolve or materialise in different ways based on the political, geographical and socio-economic context
that is assumed.

Topic 3: Workshop on AI in Work, Innovation, Productivity & Skills (AI-WIPS)
OECD Secretariat Presentation
Alistair Nolan from the Science and Technology Policy (STP) division of the Science, Technology andInnovation Directorate (STI) presented on the potential for a joint workshop (tentatively anticipated to occurin Q2 2024) with the OECD.AI Policy Observatory and OECD Strategic Foresight Unit that could involve
members of the expert group. He presented on a handful of potential workshop topics to obtain expert
opinions on the perceived relevance of the topics, and to gauge members’ initial interest in participating. It
was explained that additional topics may be added to the workshop depending on expert group progress
and interests at the time it is organised.
In particular, his presentation focused on four macro-themes: AI for better policymaking; AI in materials
sciences; AI and socially productive robots; AI and possible dual use in science.
AI for better policymaking
In the context of policymaking, large language models (LLMs) and other generative AI models could playa crucial role in informing policymakers. For instance, they could be used to produce accurate forecasts inreal-time, simulate scenarios, as well as use diverse sources of qualitative data to produce output.
Furthermore, Mr. Nolan highlighted how collective intelligence integrated with AI systems is a fruitfulinvestment area which can drive fast progress.
AI in materials sciences
Machine learning has already been used for a while in materials sciences, a scientific field focussing on
researching and discovering novel materials to drive innovation.
Challenges in the field exist, with protein folding types of problems being a primary example, where AI
systems are not yet fully able to carry out complex calculations. Breakthroughs in the field have a great
deal of potential, for instance, by aiding climate change efforts by optimising manufacturing of ultra-lightmaterials.
Further beneficial impacts of AI could be experienced in the geopolitical domain. As the domestic semiconductor manufacturing industries in the US and EU heavily rely on China's economy of scale concerning
critical minerals extraction, AI could develop alternative processes to make smaller scale extraction9
profitable. Similarly, its use might aid development efforts towards digital twins of material processes that
do not yet exist in order to discover alternative and more affordable materials to be used in place of criticalraw materials.
AI and socially productive robots
The case of socially productive robots should be analysed under the COVID-19 context. During the crisis,socially productive robots were considered as a highly effective tool to be employed in hospitals. However,
several cost-benefit analyses performed to assess whether to deploy robotics were mostly negative. An
example case is represented by disinfection robots that were employed during the crisis, which presented
substantial difficulties in navigating the physical environment and processing COVID-19 test samples
because of their different shapes. Generally speaking, robots cannot yet deal with the social environment
due to a large amount of variables, and this seems unlikely to change soon.
Dual use of AI
Mr. Nolan finally presented the risks of dual use in AI. For instance, AI models can be used in chemistrylabs to minimise toxicity of materials; however, one could also maximise the toxicity of materials, by simply
flipping algorithm and with the aid of publicly available data.

Experts feedback and conclusion
Experts subsequently reacted to the content of the presentation and shared the following insights:•

Investment in AI forecasting. In the context of AI in policymaking, governments should startinvesting in AI technology as it represents a fruitful area for public sector's service improvement interms of governance. Furthermore, the work of the group has been highlighted as a unique
instrument to stimulate change, in areas where the market or states alone will not be able toachieve the broad impact they wish achieve.

•

Focus on future-oriented work on neuro-technologies. AI combined with other technologies,
such as neurotech, has high potential to create societal benefits and should hence be the focus ofOECD work.

•

Biomedicine norms as solid basis for AI work. Bioengineering and biomedicine community
have developed a set of norms that are powerful enough to be leveraged in the work of the Expert
Group.

•

Asymmetric benefits in LLMs. LLMs performances and outcomes are heavily shaped by data
used for training. When English language is not the base, a form of dual use could materialize, inthe sense that ASEAN countries need to develop different LLMs to reap the benefits, or they will
incur in negative impacts.

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Annex A: Elements and early findings of the expert group survey
The following items were presented to expert group members based on early survey results. Additional
responses were received after the expert group meeting. As a result, some of the categories for someitems may have changed. The final responses will be published in two forthcoming OECD reports,
tentatively titled:•

Exploring Artificial Intelligence Futures: Prospective milestones, risks and benefits.

•

Exploring Artificial Intelligence Futures: Prospective solutions and governance approaches.

Annex A - List of Participants
Rebecca Anselmetti - Senior Policy Advisor at the UK government’s Department for Digital, Culture,
Media and Sport.
Azeem Azhar – Founder of Exponential View.
Joscha Bach - Principal AI Engineer at Intel Labs.
Amir Banifatemi – Co-founder, AI Commons.
Yoshua Bengio - Professor at University of Montreal and Founder and Scientific Director at MILA,
Quebec AI Institute.
Jamie Berryhill – AI Policy Analyst at OECD.
Nozha Boujemaa - Digital Trust Officer at Decathlon.
Duncan Cass-Beggs - Executive Director of the Global AI Risk Initiative at the Centre for International
Governance Innovation (CIGI).
Rumman Chowdhury - Responsible AI Fellow at Harvard University’s Berkman Klein Center.
Juraj Čorba - Senior expert, Digital Regulation & Governance - Slovak Ministry of Investments, Regional
Development and Informatization.
Mariano-Florentino (Tino) Cuéllar - President and CEO at Carnegie Endowment for International Peace.
Pam Dixon - Founder and Executive Director at World Privacy Forum.
Dexter Docherty – Strategic Foresight Analyst at OECD.
Charles Fadel - Founder & Chairman at Center for Curriculum Redesign.
Daniel Faggella - Head of Research, CEO at Emerj AI Research.
Rebecca Finlay - CEO at Partnership on AI.
Sebastian Hallensleben - Head of Digitalisation and AI at VDE Association for Electrical, Electronic &Information Technologies.
Juha Heikkilä - Head of Unit, Robotics, Directorate-General for Communication Networks, Content andTechnology at DG CONNECT, European Commission.
Hamish Hobbs - Policy Advisor to the OECD Strategic Foresight Unit.

 11
Emmanuel Kahembwe - CEO - VDE UK.
Holden Karnofsky - Director of AI Strategy at Open Philanthropy.
Ziv Katzir - Head of the National Plan for Artificial Intelligence Infrastructure at Israel’s Innovation
Authority.
Rafal Kierzenkowski - Senior Counsellor for Strategic Foresight at OECD.
Daniel Leufer - Senior Policy Analyst at Access Now.
Aaron Maniam - Fellow of Practice and Director, Digital Transformation Education at Blavatnik School ofGovernment, University of Oxford.
Sarah Myers-West - Managing Director of the AI Now Institute.
Clara Neppel - Senior Director at IEEE European Business Operations.
Sean Ó hÉigeartaigh - Interim Executive Director of CSER (Centre for the Study of Existential Risk) at
University of Cambridge.
Toby Ord - Senior Research Fellow at University of Oxford.
Karine Perset - Head of the OECD.AI, OECD Digital Economy Policy Division – OECD.
Andrea Renda - Senior Research Fellow and Head of Global Governance, Regulation, Innovation andthe Digital Economy (GRID) - Centre for European Policy Studies.
Francesca Rossi - IBM Fellow and AI Ethics Global Leader, IBM.
Stuart Russell - Professor of Computer Science at the University of California, Berkeley and Director of
the Centre for Human-Compatible Artificial Intelligence.
Michael Schönstein - Head of General Digital Policy - Federal Chancellery of Germany.
Graham Taylor - Research Director - Vector Institute for Artificial Intelligence.
Helen Toner - Director of Strategy and Foundational Research Grants at Center for Security andEmerging Technology (CSET).
Toby Walsh - Chief Scientist of UNSW.AI.