从飞机失事到算法损害：安全工程框架对负责任的ML的适用性
From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML
摘要：
机器学习（ML）系统的不当设计和部署会对用户、社会和环境造成负面的下游社会和道德影响，在此称为社会和道德风险。尽管监管机器学习系统的需求越来越大，但目前评估和缓解风险的过程是脱节和不一致的。我们采访了30名行业从业者，了解他们目前的社会和道德风险管理实践，并收集了他们对将安全工程框架应用于实践的第一反应-即系统理论过程分析（STPA）和故障模式与影响分析（FMEA）。我们的研究结果表明，STPA/FMEA可以为社会和道德风险评估和缓解过程提供适当的结构。然而，我们也发现，在ML行业快节奏的文化中集成这些框架面临着不小的挑战。我们呼吁ML研究社区加强现有框架并评估其有效性，确保ML系统对所有人都更安全。
英文摘要：
Inappropriate design and deployment of machine learning (ML) systems leads to negative downstream social and ethical impact -- described here as social and ethical risks -- for users, society and the environment. Despite the growing need to regulate ML systems, current processes for assessing and mitigating risks are disjointed and inconsistent. We interviewed 30 industry practitioners on their current social and ethical risk management practices, and collected their first reactions on adapting safety engineering frameworks into their practice -- namely, System Theoretic Process Analysis (STPA) and Failure Mode and Effects Analysis (FMEA). Our findings suggest STPA/FMEA can provide appropriate structure toward social and ethical risk assessment and mitigation processes. However, we also find nontrivial challenges in integrating such frameworks in the fast-paced culture of the ML industry. We call on the ML research community to strengthen existing frameworks and assess their efficacy, ensuring that ML systems are safer for all people.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
