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Strategies for Increasing Corporate Responsible AI Prioritization

Angelina Wang, Teresa Datta, John P. Dickerson

TL;DR

This study investigates why corporations historically underinvest in Responsible AI (RAI) and how to motivate greater resource allocation. It uses 16 semi-structured interviews across 13 firms and inductive thematic analysis to identify six high-level motivators: external cues, regulatory pressures, organizational macro-motivators, company success, culture and individuals, and implementation effort, with external cues and regulation being the most influential. The authors map these motivators to actionable levers and stakeholder actors, revealing a nuanced landscape where no single strategy guarantees adoption but a toolkit of approaches can guide practice. They propose forward-looking recommendations, including countering ethics-washing through journalist–RAI collaborations, developing interpretable metrics, and pursuing structural incentives like alternative corporate forms and research–practice integration. The work aims to inform practitioners and policymakers about leverage points to boost RAI prioritization in real-world corporate settings, while acknowledging the limits of incremental changes within existing capitalist structures.

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.

Strategies for Increasing Corporate Responsible AI Prioritization

TL;DR

This study investigates why corporations historically underinvest in Responsible AI (RAI) and how to motivate greater resource allocation. It uses 16 semi-structured interviews across 13 firms and inductive thematic analysis to identify six high-level motivators: external cues, regulatory pressures, organizational macro-motivators, company success, culture and individuals, and implementation effort, with external cues and regulation being the most influential. The authors map these motivators to actionable levers and stakeholder actors, revealing a nuanced landscape where no single strategy guarantees adoption but a toolkit of approaches can guide practice. They propose forward-looking recommendations, including countering ethics-washing through journalist–RAI collaborations, developing interpretable metrics, and pursuing structural incentives like alternative corporate forms and research–practice integration. The work aims to inform practitioners and policymakers about leverage points to boost RAI prioritization in real-world corporate settings, while acknowledging the limits of incremental changes within existing capitalist structures.

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.
Paper Structure (36 sections, 2 figures, 1 table)

This paper contains 36 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. AI Ethics in Industry
  4. Organizational Theory
  5. Methods
  6. Participant Recruiting
  7. Interview
  8. Analysis
  9. Results
  10. External Cues
  11. Publicity
  12. Public awareness
  13. Customers
  14. Regulatory Pressures
  15. Compliance
  16. ...and 21 more sections

Figures (2)

  • Figure 1: Six themes surfaced from our interviews as directions for increasing the motivation that companies have for prioritizing responsible AI. We summarize each of these six themes, and underline examples of actors that are able to act on each.
  • Figure 2: The strategies of action accessible to each key stakeholder group, organized according to level of effort.