Technical Perspective: The Impact of Auditing for Algorithmic Bias

not prove that the original audits caused the audited companies to respond, but it clearly showed it was possible to reduce bias and that routine AI development processes would benefit from increased attention to the risk of bias. In a blog post published after the original audit was released, Microsoft discussed how they had modified training data and testing practices to ensure their facial recognition software performed comparably on different skin tones and genders.d What this paper demonstrates is twofold: First is the potential for a new mode of computing research that serves an independent auditing function for algorithmic systems, perhaps akin to work in cybersecurity or safety engineering research that looks to identify, probe, measure, and spread knowledge of common system vulnerabilities to motivate remedial efforts. The second is the potential of this kind of computing research to inform and suggest future tools and modes of algorithmic governance, both internal (corporate audits or product development protocols, such as requirements for intersectional bias testing) and external (independent algorithmic audits by regulators, especially for high-risk systems). The impact of this paper is its demonstration of computing research potential to do more than propose novel techniques or results; it can probe and expose the limitations of systems already in use and impacting people’s lives, with an eye to raising the technical and professional standards for computing excellence.