Fairness, Accountability, Transparency in AI at Scale: Lessons from National Programs
The panel aims to elucidate how different national governmental programs are implementing accountability of machine learning systems in healthcare and how accountability is operationalized in different cultural settings in legislation, policy and deployment. We have representatives from three different governments, UAE, Singapore and Maldives who will discuss what accountability of AI and machine learning means in their contexts and use cases. We hope to have a fruitful conversation around FAT ML as it is operationalized across cultures, national boundaries and legislative constraints.
Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
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Open Access Status
Ahmad, M. A., Teredesai, A., & Eckert, C. (2020). Fairness, Accountability, Transparency in AI at Scale: Lessons from National Programs. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 690. https://doi.org/10.1145/3351095.3375690