Safeguarded AI: TA1.4 Sociotechnical Integration

Why this programme

As AI becomes more capable, it has the potential to power scientific breakthroughs, enhance global prosperity, and safeguard us from disasters. But only if it’s deployed wisely.

Current techniques working to mitigate the risk of advanced AI systems have serious limitations, and can’t be relied upon empirically to ensure safety. To date, very little R&D effort has gone into approaches that provide quantitative safety guarantees for AI systems, because they’re considered impossible or impractical.

What we’re shooting for

By combining scientific world models and mathematical proofs we will aim to construct a ‘gatekeeper’, an AI system tasked with understanding and reducing the risks of other AI agents.

In doing so we’ll develop quantitative safety guarantees for AI in the way we have come to expect for nuclear power and passenger aviation.

Our goal: to usher in a new era for AI safety, allowing us to unlock the full economic and social benefits of advanced AI systems while minimising risks.

The third solicitation for this programme is focused on TA1.4 Sociotechnical Integration. Backed by £3.4m, we’re looking to support teams from the economic, social, legal and political sciences to consider the sound socio-technical integration of Safeguarded AI systems.  

This solicitation seeks R&D Creators – individuals and teams that ARIA will fund – to work on problems that are plausibly critical to ensuring that the technologies developed a part of the programme will be used in the best interest of humanity at large, and that they are designed in a way that enables their governability through representative processes of collective deliberation and decision-making. 

A few examples of the open problems we’re looking for people to work on:

• Qualitative deliberation facilitation: What tools or processes best enable representative input, collective deliberation and decision-making about safety specifications, acceptable risk thresholds, or success conditions for a given application domain? We hope to integrate these into the Safeguarded AI scaffolding. 

• Quantitative bargaining solutions: What social choice mechanisms or quantitative bargaining solutions could best navigate irreconcilable differences in stakeholders’ goals, risk tolerances, and preferences, in order for Safeguarded AI systems to serve a multi-stakeholder notion of public good?

• Governability tools for society: How can we ensure that Safeguarded AI systems are governed in societally beneficial and legitimate ways?

• Governability tools for R&D organisations: Organisations developing Safeguarded AI capabilities have the potential to create significant externalities – both risks and benefits. What set of decision-making and governance mechanisms are best to ensure that entities developing or deploying Safeguarded AI capabilities have and maintain these externalities as appropriately major factors in their decision-making?

We are also open to applications proposing other lines of work which illuminate critical socio-technical dimensions of Safeguarded AI systems, if they propose solutions to increase assurance that these systems will reliably be developed and deployed in service of humanity at large.