mlsanityData to 7 Oct 2026

A Nested Model for AI Design and Validation

arXiv 2407.16888 ↗First posted 8 Jun 2024cs.CY cs.AI cs.HCcited by 8

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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

  1. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  2. 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 251
  3. 2021Bias in Machine Learning Software: Why? How? What to do?cited by 92
  4. 2020Can Federated Learning Save The Planet?cited by 52
  5. 2023Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UKcited by 13
  6. 2020Regulating Artificial Intelligence: Proposal for a Global Solutioncited by 10
  7. 2023Identifying Explanation Needs of End-users: Applying and Extending the XAI Question Bankcited by 8
  8. 2024Whom to Trust? Elective Learning for Distributed Gaussian Process Regressioncited by 3

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Citing papers per year: 2024: 1; 2025: 7.

8 papers.