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Organisations, governments, institutions and others across several jurisdictions are using AI systems for a constellation of high-stakes decisions that pose implications for human rights and civil liberties. But a fast-growing multidisciplinary scholarship on AI bias is currently documenting problems such as the discriminatory labelling and surveillance of historically marginalised subgroups. One of the ways in which AI systems generate such downstream outcomes is through their inputs. This paper focuses on a specific input dynamic which is the theoretical foundation that informs the design, operation, and outputs of such systems. The paper uses the set of technologies known as predictive policing algorithms as a case example to illustrate how theoretical assumptions can pose adverse social consequences and should therefore be systematically evaluated during audits if the objective is to detect unknown risks, avoid AI harms, and build ethical systems. In its analysis of these issues, the paper adds a new dimension to the literature on AI ethics and audits by investigating algorithmic impact in the context of underpinning theory. In doing so, the paper provides insights that can usefully inform auditing policy and practice instituted by relevant stakeholders including the developers, vendors, and procurers of AI systems as well as independent auditors.

AI audits for assessing design logics and building ethical systems: the case of predictive policing algorithms