Many stakeholders struggle to make reliances on ML-driven systems due to the risk of harm these systems may cause. Concerns of trustworthiness, unintended social harms, and unacceptable social and ethical violations undermine the promise of ML advancements. Moreover, such risks in complex ML-driven systems present a special challenge as they are often difficult to foresee, arising over periods of time, across populations, and at scale. These risks often arise not from poor ML development decisions or low performance directly but rather emerge through the interactions amongst ML development choices, the context of model use, environmental factors, and the effects of a model on its target. Systems safety engineering is an established discipline with a proven track record of identifying and managing risks even in high-complexity sociotechnical systems. In this work, we apply a state-of-the-art systems safety approach to concrete applications of ML with notable social and ethical risks to demonstrate a systematic means for meeting the assurance requirements needed to argue for safe and trustworthy ML in sociotechnical systems.

翻译：许多利益相关者因机器学习驱动系统可能造成的危害风险而难以信赖这些系统。对可信度、意外社会伤害以及不可接受的社会和伦理违规的担忧，削弱了机器学习进步的前景。此外，复杂机器学习驱动系统中的此类风险构成了特殊挑战：它们往往难以预见，会随时间推移、跨越人群并在大规模范围内显现。这些风险通常并非直接源于糟糕的机器学习开发决策或低性能，而是通过机器学习开发选择、模型使用情境、环境因素以及模型对目标对象的影响之间的相互作用而浮现。系统安全工程是一门成熟的学科，即便在高度复杂的社会技术系统中，也拥有识别和管理风险的可靠记录。在本工作中，我们将一种先进的系统安全方法应用于具有显著社会和伦理风险的具体机器学习场景，以展示一种系统化手段，从而满足在社会技术系统中论证安全与可信赖机器学习所需的保证要求。