Strategies for Increasing Corporate Responsible AI Prioritization

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.

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Institutions

Publication Details

Journal
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Published
2024-10-16
DOI
https://doi.org/10.1609/aies.v7i1.31743
Citations
2
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
1.06

Funders

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article

Strategies for Increasing Corporate Responsible AI Prioritization

Angelina Wang, Teresa Datta, John P. Dickerson
2 citations
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Big Data and Business Intelligence
1.06
article

Strategies for Increasing Corporate Responsible AI Prioritization

Angelina Wang, Teresa Datta, John P. Dickerson
article en
2 citations

Abstract

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.

Proceedings of the AAAI/ACM Conference on AI Ethics and SocietyVol. 7
Lamar State College–Port Arthur (US), Stanford University (US)
National Science Foundation
Openalex Percentile: Top 16%
Big Data and Business Intelligence
1.06
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