Preventing undesirable behavior of intelligent machines

Intelligent machines using machine learning algorithms are ubiquitous, ranging from simple data analysis and pattern recognition tools to complex systems that achieve superhuman performance on various tasks. Ensuring that they do not exhibit undesirable behavior-that they do not, for example, cause...

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Bibliographic Details
Published in:Science (American Association for the Advancement of Science) 2019-11, Vol.366 (6468), p.999-1004
Main Authors: Thomas, Philip S, Castro da Silva, Bruno, Barto, Andrew G, Giguere, Stephen, Brun, Yuriy, Brunskill, Emma
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Complex systems
Data analysis
Diabetes mellitus
Learning algorithms
Machine learning
Mathematics
Pattern analysis
Pattern recognition
Quality of life
Task complexity
Viability
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Summary:Intelligent machines using machine learning algorithms are ubiquitous, ranging from simple data analysis and pattern recognition tools to complex systems that achieve superhuman performance on various tasks. Ensuring that they do not exhibit undesirable behavior-that they do not, for example, cause harm to humans-is therefore a pressing problem. We propose a general and flexible framework for designing machine learning algorithms. This framework simplifies the problem of specifying and regulating undesirable behavior. To show the viability of this framework, we used it to create machine learning algorithms that precluded the dangerous behavior caused by standard machine learning algorithms in our experiments. Our framework for designing machine learning algorithms simplifies the safe and responsible application of machine learning.
ISSN:0036-8075
1095-9203
DOI:10.1126/science.aag3311