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Recent AI-related scandals have shed a spotlight on accountability in AI,with increasing public interest and concern. This paper draws on literaturefrom public policy and governance to make two contributions. First, we proposean AI accountability ecosystem as a useful lens on the system, with differentstakeholders requiring and contributing to specific accountability mechanisms.We argue that the present ecosystem is unbalanced, with a need for improvedtransparency via AI explainability and adequate documentation and processformalisation to support internal audit, leading up eventually to externalaccreditation processes. Second, we use a case study in the gambling sector toillustrate in a subset of the overall ecosystem the need for industry-specificaccountability principles and processes. We define and evaluate critically theimplementation of key accountability principles in the gambling industry,namely addressing algorithmic bias and model explainability, before concludingand discussing directions for future work based on our findings. Keywords:Accountability, Explainable AI, Algorithmic Bias, Regulation.

Accountability in AI: From Principles to Industry-specific Accreditation