政策设计影响模型解释质量，目的不重要，长度才关键。
The Balancing Act of Policies in Developing Machine Learning Explanations
- 通过课堂实验测试政策长度与目的对开发者遵守度的影响。
- 政策长度影响参与度，但解释质量普遍较差。
- 适合关注AI可解释性政策制定的研究者和从业者。
机器学习模型常因决策过程不透明而受到批评。本研究探讨政策设计如何影响机器学习模型的解释质量。我们开展了包含124名参与者的课堂实验，分析了政策长度和政策目的对开发者遵守政策要求的影响。结果表明，政策长度会影响部分要求的参与度，但政策目的无显著影响；整体解释质量仍然较差。研究揭示了有效政策制定的挑战，强调在解释中需兼顾多元利益相关者视角。
原文摘要 · Abstract (English)
Machine learning models are often criticized as opaque from a lack of transparency in their decision-making process. This study examines how policy design impacts the quality of explanations in ML models. We conducted a classroom experiment with 124 participants and analyzed the effects of policy length and purpose on developer compliance with policy requirements. Our results indicate that while policy length affects engagement with some requirements, policy purpose has no effect, and explanation quality is generally poor. These findings highlight the challenge of effective policy development and the importance of addressing diverse stakeholder perspectives within explanations.
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