Why putting artificial intelligence ethics into practice is not enough: Towards a multi-level framework - Hao Wang; Vincent Blok (Big Data & Society)
Artificial intelligence (AI) ethics is undergoing a practical shift towards putting principles into design practices in developing responsible AI. While this practical turn is essential, this paper highlights its potential risk of overly focusing on addressing issues at the level of individual artifacts, which can neglect more profound structural challenges and the need for significant systemic change. Such oversight makes AI ethics lose its strength in addressing some hidden, long-term harms within broader contexts. In this paper, we propose that the reflection on structural issues should be an integral part of AI ethics. To achieve this, we develop a multi-level framework to analyze socio-ethical issues of AI at both the artifact and broader structural levels. This framework can serve as a potentially transformative approach to uncover some unspoken assumptions in current AI ethics discourses and expose some blind spots in AI guidelines, policies, and regulations. Our paper paves the way to develop a practical approach that can effectively integrate this multi-level framework into real-world AI design and policymaking, ultimately bringing about transformative change.