# A Nested Model for AI Design and Validation

arXiv: https://arxiv.org/abs/2407.16888 · HTML: https://mlsanity.com/paper/2407.16888/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 83 references that are in the map.
- First posted on arXiv 8 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Bias in Machine Learning Software: Why? How? What to do?](https://mlsanity.com/paper/2105.12195/) (2021)
- [Can Federated Learning Save The Planet?](https://mlsanity.com/paper/2010.06537/) (2020)
- [Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK](https://mlsanity.com/paper/2304.11218/) (2023)
- [Regulating Artificial Intelligence: Proposal for a Global Solution](https://mlsanity.com/paper/2005.11072/) (2020)
- [Identifying Explanation Needs of End-users: Applying and Extending the XAI Question Bank](https://mlsanity.com/paper/2307.09369/) (2023)
- [Whom to Trust? Elective Learning for Distributed Gaussian Process Regression](https://mlsanity.com/paper/2402.03014/) (2024)

## Built on by (most cited first, 8 of 8)

- [AI Readiness in Healthcare through Storytelling XAI](https://mlsanity.com/paper/2410.18725/) (2024)
- [FAIR-SIGHT: Fairness Assurance in Image Recognition via Simultaneous Conformal Thresholding and Dynamic Output Repair](https://mlsanity.com/paper/2504.07395/) (2025)
- [UbiQTree: Uncertainty Quantification in XAI with Tree Ensembles](https://mlsanity.com/paper/2508.09639/) (2025)
- [Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations](https://mlsanity.com/paper/2510.17256/) (2025)
- [Surrogate Interpretable Graph for Random Decision Forests](https://mlsanity.com/paper/2506.01988/) (2025)
- [PHAX: A Structured Argumentation Framework for User-Centered Explainable AI in Public Health and Biomedical Sciences](https://mlsanity.com/paper/2507.22009/) (2025)
- [MetaMP: Seamless Metadata Enrichment and AI Application Framework for Enhanced Membrane Protein Visualization and Analysis](https://mlsanity.com/paper/2510.04776/) (2025)
- [UbiQVision: Quantifying Uncertainty in XAI for Image Recognition](https://mlsanity.com/paper/2512.20288/) (2025)

## 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.