What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research

2026-08-10

AI Summary

This study addresses the systemic challenges facing industry in implementing Responsible AI (RAI)—including fragmented understanding, practical barriers, and insufficient support—by conducting a systematic literature review of 161 empirical studies from the past six years, encompassing diverse methodologies such as interviews, surveys, workshops, and ethnography. It presents the first comprehensive synthesis of RAI practices in industrial settings. While findings indicate heightened practitioner awareness and widespread adoption of RAI toolkits, implementation remains hindered by inadequate training, uneven organizational support, and a lack of effective interventions integrated into routine workflows. The work illuminates a critical gap between the professionalization of RAI and its operationalization in practice, offering an evidence-based foundation to inform future research and policy development.

Abstract

Responsible AI (RAI) has become a central concern for technology companies, regulators, and the public. How industry practitioners interpret, implement, and sustain RAI work directly shapes the design and deployment of AI systems. As empirical scholarship examining RAI practices in industry has rapidly expanded, findings are dispersed across studies that focus on different roles, organizational contexts, and interventions. This work synthesizes current knowledge through a literature review of 161 empirical studies spanning six years, each engaging industry practitioners via interviews, surveys, workshops, ethnographies, and other methods. Our synthesis reveals both meaningful progress and persistent challenges in industry RAI practice. Practitioner awareness has increased, RAI activities have become more professionalized, and interventions such as toolkits and guidelines are more widely adopted. At the same time, practitioners continue to face substantial barriers, including limited training, uneven organizational support, and a lack of interventions tailored to day-to-day work practices. By consolidating and organizing these findings, we provide a more complete account of industry RAI than any single study to date. We conclude by discussing implications for RAI researchers, practitioners seeking to adopt effective practices, and policymakers aiming to ground governance efforts in the realities of industry contexts.

Problem

• Responsible AI
• industry practices
• empirical research
• AI governance
• practitioner challenges

Innovation

• Responsible AI
• empirical synthesis
• industry practices
• AI governance
• professionalization