Insights and recommended resources
The Intelligence Rising team is hoping to use our workshops as a simulation laboratory to explore high-level decision-making on AI development. As the available research has highlighted significant uncertainty about the possible trajectories and impacts of AI technologies, it can be useful to simulate various scenarios to anticipate potential policy responses. We believe human decision-making is contextual and can be unpredictable, and is more complex than organisational or national incentives may suggest. We think roleplaying exercises inspired by wargames are a promising method for exploring these questions.
We are in the process of writing up results from our early games for publication. Here, we present snippets from related research that has inspired the design of our roleplaying game. We highly recommend engaging with these publications in full.
Thousands of AI authors on the future of AI
Grace et al. 2024
Respondents have published at one of the top 5 global machine learning conferences.
The figure shows a random selection of 800 responses on the positivity or negativity of long-run impacts of High-Level Machine Intelligence on humanity. Each vertical bar represents one participant and the bars are sorted left to right by a weighted sum of probabilities corresponding to overall optimism. Responses range from extremely optimistic to extremely pessimistic.
Towards Safe, Ethical and Beneficial Artificial Intelligence in the European Union and Beyond
A Multifaceted Framework for Governance
Stix 2023
This thesis puts forward novel frameworks with regards to ethically informed AI governance for both academia and the policy community in the EU. It provides relevant context and investigates the international proliferation of the term ‘trustworthy AI’, as advanced by the EU in government discourse, suggesting that the EU currently holds a “first mover” advantage in this space.
It conducts an in-depth analysis and review of key policy, investment and regulatory decisions informed by ethical considerations in the EU and what this may mean for the future. It advocates for the concept of ‘Actionable Principles’ and proposes a number of elements to develop a suitable mechanism to achieve them. It also advocates for the application of an ‘incompletely theorised agreement’ for the purpose of achieving a sufficiently cohesive and strong AI policy and scholarly community, regardless of diverging perspectives on AI impact. Finally, it advocates for the importance of institution building as one component of achieving future impact on AI governance and puts forward a blueprint for potential future organisations, many of which would be tasked with the implementation and execution of the aforementioned projects.
Who will govern artificial intelligence?
Learning from the history of strategic politics in emerging technologies
Leung 2019
Artificial intelligence (AI) is a strategic general purpose technology (GPT) with the potential to deliver vast economic value and substantially affect national security. The central claim motivating this work is that the development of a strategic GPT follows a distinct pattern of politics. By modelling this pattern, we can make predictions about how the politics of AI will unfold.
The proposed model follows a life cycle of a strategic GPT. It focuses on three actors – the state, firms, and researchers. Each actor is defined by their goals, resources and constraints. The model analyses the relationships between these actors – specifically, the synergies and conflicts that emerge between them as their goals, resources, and constraints interact.
Case studies of strategic GPTs developed in the U.S. – specifically aerospace technology, biotechnology, and cryptography – show that the model captures much of history accurately. When applied to AI, the model also does well to capture political dynamics to date and motivates predictions about how we could expect the politics of AI to unfold.
Artificial Intelligence Governance Under Change
Foundations, Facets, Frameworks
Maas 2021
This dissertation explores how we may govern a changing technology, in a changing world, using governance systems that may themselves be left changed. In recent years diverse AI applications — from facial recognition to automated legal decision-making, and from computational propaganda to Lethal Autonomous Weapons Systems — have raised deep ethical, political, legal and security concerns. With growing public and policymaker attention has come a wave of governance initiatives and proposals. Nonetheless, global governance for AI remains relatively fragmented and incipient.
At this crossroads, this dissertation takes up the research question, “How should global governance for artificial intelligence account for change?” To answer this question, this dissertation draws together scholarship on technology regulation, (international) law, and global governance, in order to unpack three facets of ‘change’ that will prove critical to the global governance of AI. These three facets of change are examined through the conceptual lenses of Sociotechnical Change, Governance Disruption, and Regime Complexity.
It argues that AI governance needs to shift or adopt novel strategies — in conceptual approach, instrument choice, and instrument design — to ensure the efficacy of AI regimes in tracking AI’s sociotechnical impacts, their resilience to future AI-driven disruption to the tools, norms or broader conditions of governance, and their coherence. In this way, AI governance regimes may remain fit for change.