A Scoping Study of Evaluation Practices for Responsible AI Tools: Steps Towards Effectiveness Evaluations
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Abstract (arXiv)
Responsible design of AI systems is a shared goal across HCI and AI communities. Responsible AI (RAI) tools have been developed to support practitioners to identify, assess, and mitigate ethical issues during AI development. These tools take many forms (e.g., design playbooks, software toolkits, documentation protocols). However, research suggests that use of RAI tools is shaped by organizational contexts, raising questions about how effective such tools are in practice. To better understand how RAI tools are -- and might be -- evaluated, we conducted a qualitative analysis of 37 publications that discuss evaluations of RAI tools. We find that most evaluations focus on usability, while questions of tools' effectiveness in changing AI development are sidelined. While usability evaluations are an important approach to evaluate RAI tools, we draw on evaluation approaches from other fields to highlight developer- and community-level steps to support evaluations of RAI tools' effectiveness in shaping AI development practices and outcomes.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Jan 2024.
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Citing papers per year: 2024: 1; 2025: 0; 2026: 5.
6 papers.
- 2026ProcCtrlBench: Evaluating Process-Level Defects and Control Preservation in LLM Coding Agentscited by 5
- 2024S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Drivingcited by 1
- 2026Your Students Don't Use LLMs Like You Wish They Didcited by 1
- 2026Real-World AI Evaluation: How FRAME Generates Systematic Evidence to Resolve the Decision-Maker's Dilemmacited by 0
- 2026Compliant But Unsatisfactory: The Gap Between Auditing Standards and Practices for Probabilistic Genotyping Softwarecited 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
- 2026ProcCtrlBench: Evaluating Process-Level Defects and Control Preservation in LLM Coding Agents18 May 2026
- 2026Your Students Don't Use LLMs Like You Wish They Did26 Apr 2026
- 2026Compliant But Unsatisfactory: The Gap Between Auditing Standards and Practices for Probabilistic Genotyping Software13 Apr 2026
- 2026Real-World AI Evaluation: How FRAME Generates Systematic Evidence to Resolve the Decision-Maker's Dilemma28 Feb 2026
- 2024S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Driving16 Aug 2024