A Nested Model for AI Design and Validation
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Abstract (arXiv)
The growing AI field faces trust, transparency, fairness, and discrimination challenges. Despite the need for new regulations, there is a mismatch between regulatory science and AI, preventing a consistent framework. A five-layer nested model for AI design and validation aims to address these issues and streamline AI application design and validation, improving fairness, trust, and AI adoption. This model aligns with regulations, addresses AI practitioner's daily challenges, and offers prescriptive guidance for determining appropriate evaluation approaches by identifying unique validity threats. We have three recommendations motivated by this model: authors should distinguish between layers when claiming contributions to clarify the specific areas in which the contribution is made and to avoid confusion, authors should explicitly state upstream assumptions to ensure that the context and limitations of their AI system are clearly understood, AI venues should promote thorough testing and validation of AI systems and their compliance with regulatory requirements.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 8 of its 83 references that are in the map. First posted on arXiv 8 Jun 2024.
Builds on
8 of 83 references.
References · 8
- 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
- 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 251
- 2021Bias in Machine Learning Software: Why? How? What to do?cited by 92
- 2020Can Federated Learning Save The Planet?cited by 52
- 2023Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UKcited by 13
- 2020Regulating Artificial Intelligence: Proposal for a Global Solutioncited by 10
- 2023Identifying Explanation Needs of End-users: Applying and Extending the XAI Question Bankcited by 8
- 2024Whom to Trust? Elective Learning for Distributed Gaussian Process Regressioncited by 3
Built on by
Citing papers per year: 2024: 1; 2025: 7.
8 papers.
- 2024AI Readiness in Healthcare through Storytelling XAIcited by 4
- 2025UbiQTree: Uncertainty Quantification in XAI with Tree Ensemblescited by 4
- 2025FAIR-SIGHT: Fairness Assurance in Image Recognition via Simultaneous Conformal Thresholding and Dynamic Output Repaircited by 3
- 2025Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanationscited by 2
- 2025Surrogate Interpretable Graph for Random Decision Forestscited by 1
- 2025PHAX: A Structured Argumentation Framework for User-Centered Explainable AI in Public Health and Biomedical Sciencescited by 1
- 2025MetaMP: Seamless Metadata Enrichment and AI Application Framework for Enhanced Membrane Protein Visualization and Analysiscited by 0
- 2025UbiQVision: Quantifying Uncertainty in XAI for Image Recognitioncited by 0
- 2025UbiQVision: Quantifying Uncertainty in XAI for Image Recognition23 Dec 2025
- 2025Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations20 Oct 2025
- 2025MetaMP: Seamless Metadata Enrichment and AI Application Framework for Enhanced Membrane Protein Visualization and Analysis6 Oct 2025
- 2025UbiQTree: Uncertainty Quantification in XAI with Tree Ensembles13 Aug 2025
- 2025PHAX: A Structured Argumentation Framework for User-Centered Explainable AI in Public Health and Biomedical Sciences29 Jul 2025
- 2025Surrogate Interpretable Graph for Random Decision Forests17 May 2025
- 2025FAIR-SIGHT: Fairness Assurance in Image Recognition via Simultaneous Conformal Thresholding and Dynamic Output Repair10 Apr 2025
- 2024AI Readiness in Healthcare through Storytelling XAI24 Oct 2024