# FAIR for AI: An interdisciplinary and international community building perspective

arXiv: https://arxiv.org/abs/2210.08973 · HTML: https://mlsanity.com/paper/2210.08973/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 75 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 22 of 22)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Scientific Machine Learning Benchmarks](https://mlsanity.com/paper/2110.12773/) (2021)
- [Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics](https://mlsanity.com/paper/1706.04317/) (2017)
- [Applications of physics informed neural operators](https://mlsanity.com/paper/2203.12634/) (2022)
- [funcX: A Federated Function Serving Fabric for Science](https://mlsanity.com/paper/2005.04215/) (2020)
- [Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection](https://mlsanity.com/paper/2012.08545/) (2020)
- [DLHub: Model and Data Serving for Science](https://mlsanity.com/paper/1811.11213/) (2018)
- [Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles](https://mlsanity.com/paper/2006.12117/) (2020)
- [A FAIR and AI-ready Higgs boson decay dataset](https://mlsanity.com/paper/2108.02214/) (2021)
- [FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy](https://mlsanity.com/paper/2207.00611/) (2022)
- [Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure](https://mlsanity.com/paper/2003.08394/) (2020)
- [Explainable AI for High Energy Physics](https://mlsanity.com/paper/2206.06632/) (2022)
- [Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey](https://mlsanity.com/paper/1812.02183/) (2018)
- [Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers](https://mlsanity.com/paper/2004.09524/) (2020)
- [Inference-optimized AI and high performance computing for gravitational wave detection at scale](https://mlsanity.com/paper/2201.11133/) (2022)
- [Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms](https://mlsanity.com/paper/1912.07618/) (2019)
- [Sharing Begins at Home](https://mlsanity.com/paper/2201.06564/) (2022)
- [Playing catch-up in building an open research commons](https://mlsanity.com/paper/2208.04682/) (2022)

## Built on by (most cited first, 13 of 13)

- [SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Models](https://mlsanity.com/paper/2503.13503/) (2025)
- [FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?](https://mlsanity.com/paper/2401.11033/) (2024)
- [Building Machine Learning Challenges for Anomaly Detection in Science](https://mlsanity.com/paper/2503.02112/) (2025)
- [APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service](https://mlsanity.com/paper/2308.08786/) (2023)
- [Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction](https://mlsanity.com/paper/2509.22059/) (2025)
- [Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2)](https://mlsanity.com/paper/2505.16619/) (2025)
- [CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systems](https://mlsanity.com/paper/2508.19281/) (2025)
- [Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF](https://mlsanity.com/paper/2503.14192/) (2025)
- [Human-in-the-Loop and AI: Crowdsourcing Metadata Vocabulary for Materials Science](https://mlsanity.com/paper/2512.09895/) (2025)
- [Enhancing Transparency and Traceability in Healthcare AI: The AI Product Passport](https://mlsanity.com/paper/2512.13702/) (2025)
- [Criteria-first, semantics-later: reproducible structure discovery in image-based sciences](https://mlsanity.com/paper/2602.15712/) (2026)
- [Constructing Executable Analytical Knowledge Representations for Meta-Analysis Synthesis Using an Agentic Harness](https://mlsanity.com/paper/2608.01711/) (2026)
- [A comprehensive and trustworthy benchmark of AI methods for change detection in Earth observation](https://mlsanity.com/paper/2608.28247/) (2026)

## Abstract (arXiv)

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.