Ethics-based auditing (EBA) is a structured process whereby an entity's past or present behaviour is assessed for consistency with moral principles or norms. Recently, EBA has attracted much attention as a governance mechanism that may bridge the gap between principles and practice in AI ethics. However, important aspects of EBA – such as the feasibility and effectiveness of different auditing procedures – have yet to be substantiated by empirical research. In this article, we address this knowledge gap by providing insights from a longitudinal industry case study. Over 12 months, we observed and analysed the internal activities of AstraZeneca, a biopharmaceutical company, as it prepared for and underwent an ethics-based AI audit.

Ethics-based auditing (EBA) is a structured process whereby an entity's past or present behaviour is assessed for consistency with moral principles or norms. Recently, EBA has attracted much attention as a governance mechanism that may help to bridge the gap between principles and practice in AI ethics. However, important aspects of EBA--such as the feasibility and effectiveness of different auditing procedures--have yet to be substantiated by empirical research. In this article, we address this knowledge gap by providing insights from a longitudinal industry case study. Over 12 months, we observed and analysed the internal activities of AstraZeneca, a biopharmaceutical company, as it prepared for and underwent an ethics-based AI audit.

In continuing my trend for AI ethics from the last post, Spinoza: Building an AI Ethics Framework From First Principles, I found some early research about AI ethics audit. So, the proposal is to audit the behavior of the organization as a structured process by which an entity's behavior is assessed for consistency with relevant principles or norms. However, defining the norms is the challenge. In a nutshell, the audits focus on the rationale behind the decision, code audits entail reviewing the source code, and impact audits investigate the effects of an algorithm's outputs. The process of ethics-based auditing should be continuous, holistic, dialectic, strategic and design-driven.

A series of recent developments points towards auditing as a promising mechanism to bridge the gap between principles and practice in AI ethics. Building on ongoing discussions concerning ethics-based auditing, we offer three contributions. First, we argue that ethics-based auditing can improve the quality of decision making, increase user satisfaction, unlock growth potential, enable law-making, and relieve human suffering. Second, we highlight current best practices to support the design and implementation of ethics-based auditing: To be feasible and effective, ethics-based auditing should take the form of a continuous and constructive process, approach ethical alignment from a system perspective, and be aligned with public policies and incentives for ethically desirable behaviour. Third, we identify and discuss the constraints associated with ethics-based auditing. Only by understanding and accounting for these constraints can ethics-based auditing facilitate ethical alignment of AI, while enabling society to reap the full economic and social benefits of automation.