Title
Fairness, Accountability, Transparency in AI at Scale: Lessons from National Programs
Publication Date
1-2020
Document Type
Conference Proceeding
Abstract
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.
Publication Title
Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
DOI
10.1145/3351095.3375690
Publisher Policy
No SHERPA/RoMEO policy available
Open Access Status
Licensed
Recommended Citation
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
