披露超人级AI能力会引发不同用户反应，需因人而异设计透明策略。
Superhuman Game AI Disclosure: Expertise and Context Moderate Effects on Trust and Fairness
- 用虚拟人格卡模拟用户多样性，研究披露超人级游戏AI的影响。
- 新手在合作中因披露产生挫败感，竞技玩家则易产生依赖。
- 研究揭示透明度需匹配用户经验与场景，否则适得其反。
随着人工智能在特定任务上超越人类，披露其超人能力在公平性、问责制和信任方面带来独特挑战。然而，这种披露对不同用户态度与行为的影响尚不明确，尤其可能引发挫败或过度依赖等负面反应。本文通过使用已验证的标准化虚拟人格卡（Persona Cards），模拟多样化的用户反应与公平性视角，开展一项经伦理委员会批准的研究（N=32），考察在《星际争霸II》竞争场景中，披露超人级游戏AI能力如何影响用户行为。结果表明，透明度具有双重效应：虽可缓解怀疑，却也导致新手在合作情境中产生沮丧与战略放弃，竞技情境下则引发过度依赖；经验丰富的竞技玩家将披露视为不可战胜对手的确认，转而采取次优目标。研究公开了包含人物档案、提示语、交互日志与协议的Persona Cards数据集，以推动可复现的人机对齐研究。本工作表明，透明度并非万能解药；有效利用披露提升信任与问责，需根据用户特征、领域规范及具体公平目标进行精细设计。
原文摘要 · Abstract (English)
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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