Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts
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Abstract (arXiv)
In the evolving landscape of AI regulation, it is crucial for companies to conduct impact assessments and document their compliance through comprehensive reports. However, current reports lack grounding in regulations and often focus on specific aspects like privacy in relation to AI systems, without addressing the real-world uses of these systems. Moreover, there is no systematic effort to design and evaluate these reports with both AI practitioners and AI compliance experts. To address this gap, we conducted an iterative co-design process with 14 AI practitioners and 6 AI compliance experts and proposed a template for impact assessment reports grounded in the EU AI Act, NIST's AI Risk Management Framework, and ISO 42001 AI Management System. We evaluated the template by producing an impact assessment report for an AI-based meeting companion at a major tech company. A user study with 8 AI practitioners from the same company and 5 AI compliance experts from industry and academia revealed that our template effectively provides necessary information for impact assessments and documents the broad impacts of AI systems. Participants envisioned using the template not only at the pre-deployment stage for compliance but also as a tool to guide the design stage of AI uses.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 18 of its 56 references that are in the map. First posted on arXiv 24 Jul 2024.
Builds on
18 of 56 references.
References · 18
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6 more
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Citing papers per year: 2025: 6; 2026: 4.
10 papers.
- 2025From Hazard Identification to Controller Design: Proactive and LLM-Supported Safety Engineering for ML-Powered Systemscited by 7
- 2025Invisible Filters: Cultural Bias in Hiring Evaluations Using Large Language Modelscited by 6
- 2025AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliancecited by 6
- 2025Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Conceptscited by 4
- 2025The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harmscited by 3
- 2026How Well Can LLM Agents Simulate End-User Security and Privacy Attitudes and Behaviors?cited by 3
- 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
- 2026Agent-Supported Foresight for AI Systemic Risks: AI Agents for Breadth, Experts for Judgmentcited by 2
- 2026Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaborationcited by 2
- 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
- 2026Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaboration23 Apr 2026
- 2026Agent-Supported Foresight for AI Systemic Risks: AI Agents for Breadth, Experts for Judgment9 Feb 2026
- 2026How Well Can LLM Agents Simulate End-User Security and Privacy Attitudes and Behaviors?6 Feb 2026
- 2025AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliance1 Oct 2025
- 2025Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts27 Sep 2025
- 2025Invisible Filters: Cultural Bias in Hiring Evaluations Using Large Language Models21 Aug 2025
- 2025The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harms21 Aug 2025
- 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 Platform8 Apr 2025
- 2025From Hazard Identification to Controller Design: Proactive and LLM-Supported Safety Engineering for ML-Powered Systems11 Feb 2025