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Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter
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Good(3)Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.
Rating inherited from publication venue: Future of Life Institute
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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Articles
WINTER 2015 105
A
rtificial intelligence (AI) research has explored a variety
of problems and approaches since its inception, but for
the last 20 years or so has been focused on the prob-
lems 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 abil-
ity to make good decisions, plans, or inferences. The adop-
tion of probabilistic representations and statistical learning
methods has led to a large degree of integration and cross-
fertilization between AI, machine learning, statistics, control
theory, neuroscience, and other fields. The establishment of
shared theoretical frameworks, combined with the availabil-
ity of data and processing power, has yielded remarkable suc-
cesses in various component tasks such as speech recogni-
tion, image classification, autonomous vehicles, machine
translation, legged locomotion, and question-answering sys-
tems.
Copyright © 2015, Association for the Advancement of Artificial Intelligence. All rights reserved. ISSN 0738-4602
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 inves-
tigate 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.
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Articles
106 AI MAGAZINE
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 con-
sensus that AI research is progressing steadily, and
that its impact on society is likely to increase. The
potential benefits are huge, since everything that civ-
ilization has to offer is a product of human intelli-
gence; 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 Presi-
dential Panel on Long-Term AI Futures (Horvitz and
Selman 2009) and other projects and community
efforts on AI’s future impacts. These constitute a sig-
nificant expansion of the field of AI itself, which up
to now has focused largely on techni
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