# A Scoping Study of Evaluation Practices for Responsible AI Tools: Steps Towards Effectiveness Evaluations

arXiv: https://arxiv.org/abs/2401.17486 · HTML: https://mlsanity.com/paper/2401.17486/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2024.

## Built on by (most cited first, 6 of 6)

- [ProcCtrlBench: Evaluating Process-Level Defects and Control Preservation in LLM Coding Agents](https://mlsanity.com/paper/2605.20251/) (2026)
- [S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Driving](https://mlsanity.com/paper/2408.08584/) (2024)
- [Your Students Don't Use LLMs Like You Wish They Did](https://mlsanity.com/paper/2604.23486/) (2026)
- [Real-World AI Evaluation: How FRAME Generates Systematic Evidence to Resolve the Decision-Maker's Dilemma](https://mlsanity.com/paper/2603.13294/) (2026)
- [Compliant But Unsatisfactory: The Gap Between Auditing Standards and Practices for Probabilistic Genotyping Software](https://mlsanity.com/paper/2604.10875/) (2026)
- [What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research](https://mlsanity.com/paper/2608.10431/) (2026)

## 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.