Helen Toner

Understanding and advising legal practitioners on the long-term challenges of AI in the judiciary.

This funding will support an early project of the Legal Priorities Project (LPP), a new organization that describes its mission as "...to conduct legal research that tackles the world's most pressing problems, [which] currently leads us to focus on the protection of future generations." Nick applied for funding to work with an organization that advises the Indian Supreme Court, to carry out a project researching and eventually advising on the long-term challenges of using AI in judicial systems.

I'm excited about LPP's potential to build out legal priorities research as a new area, and this specific project seems like an interesting approach to try. I think it's especially good for this to be done in collaboration with practitioners (in this case, advisors and/or staff of the Indian Supreme Court), because in my view, the feedback loops enabled by this kind of arrangement make it more likely that the work produced is useful and relevant to the real world. I see this grant as an experiment and as an investment in exploring what types of work LPP can facilitate.

Supporting Jess Whittlestone's work on a project regarding 'mid-term AI impacts'.

Sam applied for funding to cover relocation expenses from New Zealand to Cambridge, UK, in order to begin his role there as a research assistant supporting Jess Whittlestone's work at the Centre for the Future of Intelligence, which we have supported in the past. The fund generally tries to avoid paying for costs that we would expect employers to cover, to avoid creating bad incentives. In this case, however, Cambridge University apparently only covers moving expenses for contracts over 2 years, and this is a 1-year position. We therefore decided that this was a reasonable use of funds.

Identifying and resolving tensions between competition law and long-term AI strategy.

Shin-Shin Hua and Haydn Belfield proposed a research project on the implications of competition law for long-term AI governance. One important set of questions in AI governance revolves around the number and type of actors (e.g. companies, governments, international organizations) involved in developing advanced AI systems. Competition law likely affects the types of intervention that are possible on this axis, e.g. whether multiple research groups could combine into a consortium-like structure. This broad topic has been discussed in the long-term AI governance space for a while, but so far very little serious work has been done on it.

Shin-Shin has 7 years of experience as a competition lawyer, and also has experience in academic legal research involving AI. Haydn is an Academic Project Manager at the Centre for the Study of Existential Risk.

I think good quality work on this topic would be valuable for the field of long-term AI governance, and I believe this is a promising team to undertake such work.

General Support

The Machine Intelligence Research Institute (MIRI) does computer science and math research on AI alignment. MIRI is a challenging organization to evaluate, both because their public work is hard to evaluate (because much of it does not fit neatly within an existing discipline, and because much of the case for why the research matters is based on hypotheses about how AI research will develop in the future) and because they have decided to make much of their research non-public, for strategic reasons.

Nonetheless, I believe there is sufficient evidence that MIRI is doing good work that it makes sense for the Fund to support them. This evidence includes the track record of their team, the quality of some recent hires, and some promising signs about their ability to produce work that well-established external reviewers consider to be very high-quality—most notably, the acceptance of one of their decision theory papers to a top philosophy journal, The Journal of Philosophy.

Organizing a "Human-Aligned AI” event at NeurIPS.

Grant date: November 2019

Claudia Shi and Victor Veitch applied for funding to run a social event themed around “Human-aligned AI” at the machine learning conference NeurIPS in December 2019. The aim of the event was to provide a space for NeurIPS attendees who care about doing high-impact projects and/or about long-term AI safety to gather and discuss these topics.

I believe that holding events like this is an easy way to do a very basic form of “field-building,” by making it easier for machine learning researchers who are interested in longtermism and related topics to find each other, discuss their work, and perhaps work together in the future or change their research plans. Our funding was mainly used to cover catering for the 100-person event, which we hoped would make the event more enjoyable for participants and therefore more effective in facilitating discussions and connections. After the event, the organizers had $1863 left over, which they returned to the Fund.

A two-day, career-focused workshop to inform and connect European EAs interested in AI governance

Alex Lintz and some collaborators from EA Zürich proposed organizing a two-day workshop for EAs interested in AI governance careers, with the goals of giving participants background on the space, offering career advice, and building community. We agree with their assessment that this space is immature and hard to enter, and believe their suggested plan for the workshop looks like a promising way to help participants orient to careers in AI governance.