用对比解释帮人看清自己与AI的差异，提升决策能力
Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills
- 生成预测人类错误思路的对比解释，揭示AI与人的决策差异
- 实验显示对比解释使用户独立决策能力显著提升（N=628）
- 适合关注人机协作中技能保留的研究者与产品设计者
人们在依赖AI进行决策支持时，决策能力常无法提升甚至退化，即使AI提供了有信息量的解释。我们认为部分原因是人们本能寻求对比性解释——即厘清AI判断与自身推理之间的差异；而现有AI系统多提供单向解释，仅说明为何AI正确，却未考虑用户思维。为此，我们提出一种以人为中心的对比解释框架，通过预测可能的人类错误判断，解释AI选择与该预测人类选择之间的差异。大规模实验（N=628）表明，相比单向解释，对比解释能显著提升用户独立决策能力，且不牺牲决策准确性。在日益担忧技能退化的背景下，本研究证明将人类推理融入AI设计，有助于促进人类技能发展。
原文摘要 · Abstract (English)
People's decision-making abilities often fail to improve or may even erode when they rely on AI for decision-support, even when the AI provides informative explanations. We argue this is partly because people intuitively seek contrastive explanations, which clarify the difference between the AI's decision and their own reasoning, while most AI systems offer "unilateral" explanations that justify the AI's decision but do not account for users' thinking. To align human-AI knowledge on decision tasks, we introduce a framework for generating human-centered contrastive explanations that explain the difference between AI's choice and a predicted, likely human choice about the same task. Results from a large-scale experiment (N = 628) demonstrate that contrastive explanations significantly enhance users' independent decision-making skills compared to unilateral explanations, without sacrificing decision accuracy. Amid rising deskilling concerns, our research demonstrates that incorporating human reasoning into AI design can foster human skill development.
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