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

TL;DR

This paper explores how safety engineering frameworks like STPA and FMEA can be adapted to improve social and ethical risk management in machine learning systems, highlighting potential benefits and integration challenges.

Contribution

It introduces the application of safety engineering frameworks to ML risk assessment and discusses practical challenges faced during integration in industry practices.

Findings

STPA/FMEA can structure social and ethical risk assessments

Practitioners see potential in safety frameworks for ML

Challenges exist in adapting safety frameworks to fast-paced ML industry

Abstract

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.