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Inappropriate design and deployment of machine learning (ML) systems leads tonegative downstream social and ethical impact -- described here as social andethical risks -- for users, society and the environment. Despite the growingneed to regulate ML systems, current processes for assessing and mitigatingrisks are disjointed and inconsistent. We interviewed 30 industry practitionerson their current social and ethical risk management practices, and collectedtheir first reactions on adapting safety engineering frameworks into theirpractice -- namely, System Theoretic Process Analysis (STPA) and Failure Modeand Effects Analysis (FMEA). Our findings suggest STPA/FMEA can provideappropriate structure toward social and ethical risk assessment and mitigationprocesses. However, we also find nontrivial challenges in integrating suchframeworks in the fast-paced culture of the ML industry. We call on the MLresearch community to strengthen existing frameworks and assess their efficacy,ensuring that ML systems are safer for all people.

From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML