# Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty

arXiv: https://arxiv.org/abs/2303.07462 · HTML: https://mlsanity.com/paper/2303.07462/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 40 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Accelerating Self-Play Learning in Go](https://mlsanity.com/paper/1902.10565/) (2019)
- [Does Explainable Artificial Intelligence Improve Human Decision-Making?](https://mlsanity.com/paper/2006.11194/) (2020)
- [Boosting human decision-making with AI-generated decision aids](https://mlsanity.com/paper/2203.02776/) (2022)

## Built on by (most cited first, 27 of 27)

- [Getting aligned on representational alignment](https://mlsanity.com/paper/2310.13018/) (2023)
- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [Open-Endedness is Essential for Artificial Superhuman Intelligence](https://mlsanity.com/paper/2406.04268/) (2024)
- [Empirical evidence of Large Language Model's influence on human spoken communication](https://mlsanity.com/paper/2409.01754/) (2024)
- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [ChatGPT Rates Natural Language Explanation Quality Like Humans: But on Which Scales?](https://mlsanity.com/paper/2403.17368/) (2024)
- [Diversifying AI: Towards Creative Chess with AlphaZero](https://mlsanity.com/paper/2308.09175/) (2023)
- [Keep the Faith: Faithful Explanations in Convolutional Neural Networks for Case-Based Reasoning](https://mlsanity.com/paper/2312.09783/) (2023)
- [Map-based Experience Replay: A Memory-Efficient Solution to Catastrophic Forgetting in Reinforcement Learning](https://mlsanity.com/paper/2305.02054/) (2023)
- [AI Agent Behavioral Science](https://mlsanity.com/paper/2506.06366/) (2025)
- [Explain the Black Box for the Sake of Science: the Scientific Method in the Era of Generative Artificial Intelligence](https://mlsanity.com/paper/2406.10557/) (2024)
- [AI systems out-persuade expert humans](https://mlsanity.com/paper/2606.16475/) (2026)
- [Benchmarking ChatGPT-4 on ACR Radiation Oncology In-Training (TXIT) Exam and Red Journal Gray Zone Cases: Potentials and Challenges for AI-Assisted Medical Education and Decision Making in Radiation Oncology](https://mlsanity.com/paper/2304.11957/) (2023)
- [The Responsible Development of Automated Student Feedback with Generative AI](https://mlsanity.com/paper/2308.15334/) (2023)
- [(Human) Attention Is (Still) All You Need: Human oversight makes AI-assisted social science reliable](https://mlsanity.com/paper/2606.12848/) (2026)
- [Do GPT Language Models Suffer From Split Personality Disorder? The Advent Of Substrate-Free Psychometrics](https://mlsanity.com/paper/2408.07377/) (2024)
- [Tracing the Thought of a Grandmaster-level Chess-Playing Transformer](https://mlsanity.com/paper/2604.10158/) (2026)
- [New Insights into Automatic Treatment Planning for Cancer Radiotherapy Using Explainable Artificial Intelligence](https://mlsanity.com/paper/2508.14229/) (2025)
- [The Variance Paradox: How AI Reduces Diversity but Increases Novelty](https://mlsanity.com/paper/2508.19264/) (2025)
- [Dream Content Discovery from Reddit with an Unsupervised Mixed-Method Approach](https://mlsanity.com/paper/2307.04167/) (2023)
- [Who Benefits from AI? Self-Selection, Skill Gap, and the Hidden Costs of AI Feedback](https://mlsanity.com/paper/2409.18660/) (2024)
- [Assistive AI for Augmenting Human Decision-making](https://mlsanity.com/paper/2410.14353/) (2024)
- [Alien Recombination: Exploring Concept Blends Beyond Human Cognitive Availability in Visual Art](https://mlsanity.com/paper/2411.11494/) (2024)
- [Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI](https://mlsanity.com/paper/2506.15468/) (2025)
- [How Much Can a Few Engine Moves Help? Quantifying Limited Cheating in Chess](https://mlsanity.com/paper/2601.05386/) (2026)
- [Tandem Reinforcement Learning with Verifiable Rewards](https://mlsanity.com/paper/2606.28166/) (2026)
- [Collective creativity in hybrid societies](https://mlsanity.com/paper/2609.02620/) (2026)

## Abstract (arXiv)

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 years (1950-2021). 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.