Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter

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Future of Life Institute·futureoflife.org/data/documents/research_priorities.pdf

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This 2015 open letter co-authored by Stuart Russell is widely considered a foundational document in AI safety, helping establish research priorities and build community consensus around beneficial AI; it is closely associated with the Future of Life Institute's early work.

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Importance: 88/100organizational reportprimary source

Summary

A foundational 2015 document by Russell, Dewey, and Tegmark outlining research priorities to ensure AI remains robust and beneficial to society. It covers short-term priorities in economics, law, ethics, and computer science, as well as long-term considerations around AI safety. The document served as the basis for an open letter gathering nearly 7,000 signatures and helped galvanize the field of AI safety research.

Key Points

•Argues that as AI capabilities advance, research must shift focus from pure capability improvement to maximizing societal benefit and safety.
•Identifies short-term research priorities: optimizing economic impact, legal and ethical frameworks, and computer science methods for robust AI.
•Emphasizes that beneficial AI research is inherently interdisciplinary, spanning economics, law, philosophy, formal methods, and computer security.
•Introduced the framing that AI systems must reliably 'do what we want them to do,' anticipating the alignment problem.
•Originated from the 2015 Future of AI conference and became a rallying document for the emerging AI safety community.

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# Research Priorities for Robust and Beneficial Artificial Intelligence

Stuart Russell, Daniel Dewey, Max Tegmark ■ Success in the quest for artificial intelligence has the potential to bring unprecedented benefits to humanity, and it is therefore worthwhile to investigate how to maximize these benefits while avoiding potential pitfalls. This article gives numerous examples (which should by no means be construed as an exhaustive list) of such worthwhile research aimed at ensuring that AI remains robust and beneficial.

Atrothfifepicrliaoasbltlie2nm0teslylaeingaerdsnacoperp(srAoIa)hcraheseesbaseriecnhcefhoiatcsuiesnxecpdeloportnieodtnha,evbauprtrioeftobyrlems surrounding the construction of intelligent agents — systems that perceive and act in some environment. In this context, the criterion for intelligence is related to statistical and economic notions of rationality — colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic representations and statistical learning methods has led to a large degree of integration and crossfertilization between AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems.

As capabilities in these areas and others cross the threshold from laboratory research to economically valuable technologies, a virtuous cycle takes hold whereby even small improvements in performance have significant economic value, prompting greater investments in research. There is now a broad consensus that AI research is progressing steadily, and that its impact on society is likely to increase. The potential benefits are huge, since everything that civilization has to offer is a product of human intelligence; we cannot predict what we might achieve when this intelligence is magnified by the tools AI may provide, but the eradication of disease and poverty is not unfathomable. Because of the great potential of AI, it is valuable to investigate how to reap its benefits while avoiding potential pitfalls.

Progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI. Such considerations motivated the AAAI 2008–09 Presidential Panel on Long-Term AI Futures (Horvitz and Selman 2009) and other projects and community efforts on AI’s future impacts. These constitute a significant expansion of the field of AI itself, which up to now has focused largely on techniques that are neutral with respect to purpose. The present document can be viewed as a natural continuation of these efforts, focusing on identifying research directions that can help maximize the societal benefit of AI. This research i

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