This paper focuses on two highly publicized formal trade-offs in the field of responsible artificial intelligence (AI) -- between predictive accuracy and fairness and between predictive accuracy and interpretability. These formal trade-offs are often taken by researchers, practitioners, and policy-makers to directly imply corresponding tensions between underlying values. Thus interpreted, the trade-offs have formed a core focus of normative engagement in AI governance, accompanied by a particular division of labor along disciplinary lines. This paper argues against this prevalent interpretation by drawing attention to three sets of considerations that are critical for bridging the gap between these formal trade-offs and their practical impacts on relevant values. I show how neglecting these considerations can distort our normative deliberations, and result in costly and misaligned interventions and justifications. Taken together, these considerations form a sociotechnical framework that could guide those involved in AI governance to assess how, in many cases, we can and should have higher aspirations than the prevalent interpretation of the trade-offs would suggest. I end by drawing out the normative opportunities and challenges that emerge out of these considerations, and highlighting the imperative of interdisciplinary collaboration in fostering responsible AI.

翻译：本文聚焦于负责任人工智能领域中两个备受关注的正式权衡——预测准确性与公平性之间的权衡，以及预测准确性与可解释性之间的权衡。研究者、实践者和政策制定者常常认为这些正式权衡直接暗示了基础价值观之间的相应张力。在此解读下，这些权衡已成为人工智能治理规范性参与的核心焦点，并伴随着特定的学科分工。本文通过强调三类至关重要的考量因素来质疑这一主流解读，这些因素对于弥合正式权衡与其对相关价值观的实际影响之间的差距至关重要。我展示了忽视这些考量如何扭曲我们的规范性审慎，并导致代价高昂且失当的干预措施及论证依据。综合来看，这些考量构成了一个社会技术框架，能够指导人工智能治理的相关参与者评估：在许多情况下，我们如何、并且应当对权衡持有比主流解读所暗示的更高期待。最后，我总结了这些考量所带来的规范性机遇与挑战，并强调了跨学科合作在推动负责任人工智能发展中的必要性。