Strategies for Increasing Corporate Responsible AI Prioritization
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Abstract (arXiv)
Responsible artificial intelligence (RAI) is increasingly recognized as a critical concern. However, the level of corporate RAI prioritization has not kept pace. In this work, we conduct 16 semi-structured interviews with practitioners to investigate what has historically motivated companies to increase the prioritization of RAI. What emerges is a complex story of conflicting and varied factors, but we bring structure to the narrative by highlighting the different strategies available to employ, and point to the actors with access to each. While there are no guaranteed steps for increasing RAI prioritization, we paint the current landscape of motivators so that practitioners can learn from each other, and put forth our own selection of promising directions forward.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 May 2024.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 3.
6 papers.
- 2025Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learningcited by 5
- 2024A Taxonomy of Challenges to Curating Fair Datasetscited by 2
- 2025Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systemscited by 0
- 2026The Limits of AI Data Transparency Policy: Three Disclosure Fallaciescited by 0
- 2026RCTs for Frontier AI Governance: Methodological Challenges and Solutions for Human Uplift Studiescited by 0
- 2026What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Researchcited by 0
- 2026What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research11 Aug 2026
- 2026RCTs for Frontier AI Governance: Methodological Challenges and Solutions for Human Uplift Studies11 Mar 2026
- 2026The Limits of AI Data Transparency Policy: Three Disclosure Fallacies26 Jan 2026
- 2025Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems26 May 2025
- 2025Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learning7 May 2025
- 2024A Taxonomy of Challenges to Curating Fair Datasets10 Jun 2024