Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practices
Bogdana Rakova, Jingying Yang, Henriette Cramer, Rumman Chowdhury

TL;DR
This paper explores how organizational culture and structure influence the success of responsible AI efforts in industry, based on interviews with practitioners and analysis of current and future practices.
Contribution
It provides a framework for understanding organizational enablers and barriers to responsible AI implementation in large technology companies.
Findings
Organizational culture significantly impacts responsible AI effectiveness.
Certain structures support better ethical AI practices.
Transition strategies from current to ideal practices are identified.
Abstract
Large and ever-evolving technology companies continue to invest more time and resources to incorporate responsible Artificial Intelligence (AI) into production-ready systems to increase algorithmic accountability. This paper examines and seeks to offer a framework for analyzing how organizational culture and structure impact the effectiveness of responsible AI initiatives in practice. We present the results of semi-structured qualitative interviews with practitioners working in industry, investigating common challenges, ethical tensions, and effective enablers for responsible AI initiatives. Focusing on major companies developing or utilizing AI, we have mapped what organizational structures currently support or hinder responsible AI initiatives, what aspirational future processes and structures would best enable effective initiatives, and what key elements comprise the transition from…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEthics and Social Impacts of AI · Innovation, Sustainability, Human-Machine Systems · Supply Chain Resilience and Risk Management
