Superhuman artificial intelligence can improve human decision-making by increasing novelty

How will superhuman artificial intelligence (AI) affect human decision-making? And what will be the mechanisms behind this effect? We address these questions in a domain where AI already exceeds human performance, analyzing more than 5.8 million move decisions made by professional Go players over the past 71 y (1950 to 2021). To address the first question, we use a superhuman AI program to estimate the quality of human decisions across time, generating 58 billion counterfactual game patterns and comparing the win rates of actual human decisions with those of counterfactual AI decisions. We find that humans began to make significantly better decisions following the advent of superhuman AI. We then examine human players' strategies across time and find that novel decisions (i.e., previously unobserved moves) occurred more frequently and became associated with higher decision quality after the advent of superhuman AI. Our findings suggest that the development of superhuman AI programs may have prompted human players to break away from traditional strategies and induced them to explore novel moves, which in turn may have improved their decision-making.

[1]  Paul M. Krueger,et al.  Leveraging artificial intelligence to improve people’s planning strategies , 2022, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Bas van Opheusden,et al.  Boosting Human Decision-making with AI-Generated Decision Aids , 2022, Computational Brain & Behavior.

[3]  E. Cascetta,et al.  Do autonomous vehicles drive like humans? A Turing approach and an application to SAE automation Level 2 cars , 2022, Transportation Research Part C: Emerging Technologies.

[4]  Thomas F. Müller,et al.  Hybrid social learning in human-algorithm cultural transmission , 2021, Philosophical Transactions of the Royal Society A.

[5]  Sukwoong Choi,et al.  How Does AI Improve Human Decision-Making? Evidence from the AI-Powered Go Program , 2021, SSRN Electronic Journal.

[6]  Oriol Vinyals,et al.  Highly accurate protein structure prediction with AlphaFold , 2021, Nature.

[7]  Jin Kim,et al.  Human Learning from Artificial Intelligence: Evidence from Human Go Players' Decisions after AlphaGo , 2021 .

[8]  T. Griffiths,et al.  Human biases limit cumulative innovation , 2021, Proceedings of the Royal Society B.

[9]  Murat Kantarcioglu,et al.  Does Explainable Artificial Intelligence Improve Human Decision-Making? , 2020, AAAI.

[10]  Mark Chen,et al.  Language Models are Few-Shot Learners , 2020, NeurIPS.

[11]  David S. Melnick,et al.  International evaluation of an AI system for breast cancer screening , 2020, Nature.

[12]  Dale Purves,et al.  Opinion: What does AI’s success playing complex board games tell brain scientists? , 2019, Proceedings of the National Academy of Sciences.

[13]  F. Gobet,et al.  How Artificial Intelligence Can Help Us Understand Human Creativity , 2019, Front. Psychol..

[14]  C. Toumazou,et al.  Artificial intelligence can improve decision-making in infection management , 2019, Nature Human Behaviour.

[15]  David J. Wu,et al.  Accelerating Self-Play Learning in Go , 2019, ArXiv.

[16]  Demis Hassabis,et al.  Mastering the game of Go without human knowledge , 2017, Nature.

[17]  C. Legare Cumulative cultural learning: Development and diversity , 2017, Proceedings of the National Academy of Sciences.

[18]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[19]  Calvin Thigpen,et al.  Strategic Social Learning and the Population Dynamics of Human Behavior: The Game of Go , 2014, bioRxiv.

[20]  Fernand Gobet,et al.  Measuring Chess Experts' Single-Use Sequence Knowledge: An Archival Study of Departure from ‘Theoretical’ Openings , 2011, PloS one.

[21]  J. Henrich,et al.  The cultural niche: Why social learning is essential for human adaptation , 2011, Proceedings of the National Academy of Sciences.

[22]  H. Roche,et al.  Why Copy Others? Insights from the Social Learning Strategies Tournament , 2010 .

[23]  P. McLeod,et al.  Inflexibility of experts—Reality or myth? Quantifying the Einstellung effect in chess masters , 2008, Cognitive Psychology.

[24]  T. Griffiths,et al.  Iterated learning: Intergenerational knowledge transmission reveals inductive biases , 2007, Psychonomic bulletin & review.

[25]  Francisco Câmara Pereira Creativity and Artificial Intelligence , 2007 .

[26]  Martin H. Levinson Not by Genes Alone: How Culture Transformed Human Evolution , 2006 .

[27]  HuangTimothy The game of go , 2003 .

[28]  Douglas Brown,et al.  The Mammoth Book of Chess , 1998 .

[29]  A. Mahowald Development and Diversity , 1997, Science.

[30]  David Hooper,et al.  The Oxford Companion to Chess , 1984 .