mlsanityData to 7 Oct 2026

Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practices

arXiv 2006.12358 ↗First posted 22 Jun 2020cs.CYcited by 38

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Abstract (arXiv)

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 current work practices to the aspirational future.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jun 2020.

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Citing papers per year: 2021: 1; 2022: 9; 2023: 12; 2024: 4; 2025: 7; 2026: 5.

38 papers.

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  1. 2022Interactive Model Cards: A Human-Centered Approach to Model Documentationcited by 61
  2. 2022Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkitscited by 33
  3. 2022Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practicecited by 30
  4. 2022Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desideratacited by 28
  5. 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototypingcited by 28
  6. 2022From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible MLcited by 26
  7. 2023Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiencescited by 21
  8. 2022Seamful XAI: Operationalizing Seamful Design in Explainable AIcited by 20
  9. 2023Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experiencecited by 18
  10. 2023`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Valuescited by 18
  11. 2023User Experience Design Professionals' Perceptions of Generative Artificial Intelligencecited by 16
  12. 2023Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"cited by 15
26 more
  1. 2023Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practicecited by 15
  2. 2024Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Productscited by 11
  3. 2022A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunitiescited by 8
  4. 2021Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutionscited by 7
  5. 2022Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliancecited by 7
  6. 2025From Hazard Identification to Controller Design: Proactive and LLM-Supported Safety Engineering for ML-Powered Systemscited by 7
  7. 2023Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Educationcited by 5
  8. 2024Law and the Emerging Political Economy of Algorithmic Auditscited by 5
  9. 2025Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practicecited by 5
  10. 2022How Platform-User Power Relations Shape Algorithmic Accountability: A Case Study of Instant Loan Platforms and Financially Stressed Users in Indiacited by 4
  11. 2023SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebookscited by 3
  12. 2023FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelinescited by 3
  13. 2025Vibe Coding in Product Teams: Reconfiguring AI-Assisted Workflows, Prototyping, and Collaborationcited by 3
  14. 2023MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualitiescited by 2
  15. 2023Machine Learning practices and infrastructurescited by 2
  16. 2023Making Data Work Countcited by 2
  17. 2025Talking About the Assumption in the Roomcited by 2
  18. 2025The Hall of AI Fears and Hopes: Comparing the Views of AI Influencers and those of Members of the U.S. Public Through an Interactive Platformcited by 2
  19. 2026Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaborationcited by 2
  20. 2024From Expectation to Habit: Why Do Software Practitioners Adopt Fairness Toolkits?cited by 1
  21. 2025AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stagescited by 1
  22. 2026From Reflection to Repair: A Scoping Review of Dataset Documentation Toolscited by 1
  23. 2025Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systemscited by 0
  24. 2026Compliant But Unsatisfactory: The Gap Between Auditing Standards and Practices for Probabilistic Genotyping Softwarecited by 0
  25. 2026To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systemscited by 0
  26. 2026Designing a Boundary Negotiating Artifact for Collaborative Socio-Technical Sense-Making in AI Regulatory Sandboxescited by 0