The first video shows a man who thinks he's talking to a woman (bottom right corner) but is actually talking to a man (top left corner) and the second videos is deepfake demo. New generative Artificial Intelligence (AI) systems have captivated the world's imagination with promise and potential. AI's ability to analyze vast amounts of data and make autonomous decisions is a source of both awe and anxiety. People worry about bias in decision-making, the invasion of privacy, job displacement, and even the existential fear of machines becoming uncontrollable. How can we make sure AI benefits society? The National Telecommunications and Information Administration (NTIA) has responded by seeking input on how to ensure that AI companies are "accountable."

Recent AI-related scandals have shed a spotlight on accountability in AI, with increasing public interest and concern. This paper draws on literature from public policy and governance to make two contributions. First, we propose an AI accountability ecosystem as a useful lens on the system, with different stakeholders requiring and contributing to specific accountability mechanisms. We argue that the present ecosystem is unbalanced, with a need for improved transparency via AI explainability and adequate documentation and process formalisation to support internal audit, leading up eventually to external accreditation processes. Second, we use a case study in the gambling sector to illustrate in a subset of the overall ecosystem the need for industry-specific accountability principles and processes. We define and evaluate critically the implementation of key accountability principles in the gambling industry, namely addressing algorithmic bias and model explainability, before concluding and discussing directions for future work based on our findings. Keywords: Accountability, Explainable AI, Algorithmic Bias, Regulation.