Beyond Fairness Metrics: Roadblocks and Challenges for Ethical AI in Practice
It highlights systemic barriers to ethical AI in real-world applications, which is an incremental analysis focusing on non-technical factors.
The paper identifies practical challenges in deploying ethical AI at scale, such as regulatory pressures and business conflicts, and argues that current research fails to address these operational risks, calling for a holistic approach to ethics in AI development.
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.