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Superhuman Game AI Disclosure: Expertise and Context Moderate Effects on Trust and Fairness

arXiv 2503.15514 ↗First posted 31 Jan 2025cs.HC cs.AI cs.CLcited by 0

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Abstract (arXiv)

As artificial intelligence surpasses human performance in select tasks, disclosing superhuman capabilities poses distinct challenges for fairness, accountability, and trust. However, the impact of such disclosures on diverse user attitudes and behaviors remains unclear, particularly concerning potential negative reactions like discouragement or overreliance. This paper investigates these effects by utilizing Persona Cards: a validated, standardized set of synthetic personas designed to simulate diverse user reactions and fairness perspectives. We conducted an ethics board-approved study (N=32), utilizing these personas to investigate how capability disclosure influenced behaviors with a superhuman game AI in competitive StarCraft II scenarios. Our results reveal transparency is double-edged: while disclosure could alleviate suspicion, it also provoked frustration and strategic defeatism among novices in cooperative scenarios, as well as overreliance in competitive contexts. Experienced and competitive players interpreted disclosure as confirmation of an unbeatable opponent, shifting to suboptimal goals. We release the Persona Cards Dataset, including profiles, prompts, interaction logs, and protocols, to foster reproducible research into human alignment AI design. This work demonstrates that transparency is not a cure-all; successfully leveraging disclosure to enhance trust and accountability requires careful tailoring to user characteristics, domain norms, and specific fairness objectives.

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References · 16

  1. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  3. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
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  5. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 399
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  7. 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 253
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  10. 2020Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIcited by 167
  11. 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 123
  12. 2019Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approachescited by 101
4 more
  1. 2018The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communitiescited by 78
  2. 2023Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systemscited by 41
  3. 2024MisinfoEval: Generative AI in the Era of "Alternative Facts"cited by 6
  4. 2023Blaming Humans and Machines: What Shapes People's Reactions to Algorithmic Harmcited by 5

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