Beyond Fairness Metrics: Roadblocks and Challenges for Ethical AI in Practice
arXiv:2108.06217
Abstract
We review practical challenges in building and deploying ethical AI at the scale of contemporary industrial and societal uses. Apart from the purely technical concerns that are the usual focus of academic research, the operational challenges of inconsistent regulatory pressures, conflicting business goals, data quality issues, development processes, systems integration practices, and the scale of deployment all conspire to create new ethical risks. Such ethical concerns arising from these practical considerations are not adequately addressed by existing research results. We argue that a holistic consideration of ethics in the development and deployment of AI systems is necessary for building ethical AI in practice, and exhort researchers to consider the full operational contexts of AI systems when assessing ethical risks.
arXiv comment6 pages; presented at ACM SIGKDD Workshop on Machine Learning in Finance (KDD-MLF) 2021
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