mlsanityData to 7 Oct 2026

FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity

arXiv 1808.07261 ↗First posted 22 Aug 2018cs.CY cs.AIcited by 31

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Abstract (arXiv)

Accuracy is an important concern for suppliers of artificial intelligence (AI) services, but considerations beyond accuracy, such as safety (which includes fairness and explainability), security, and provenance, are also critical elements to engender consumers' trust in a service. Many industries use transparent, standardized, but often not legally required documents called supplier's declarations of conformity (SDoCs) to describe the lineage of a product along with the safety and performance testing it has undergone. SDoCs may be considered multi-dimensional fact sheets that capture and quantify various aspects of the product and its development to make it worthy of consumers' trust. Inspired by this practice, we propose FactSheets to help increase trust in AI services. We envision such documents to contain purpose, performance, safety, security, and provenance information to be completed by AI service providers for examination by consumers. We suggest a comprehensive set of declaration items tailored to AI and provide examples for two fictitious AI services in the appendix of the paper.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Aug 2018.

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Citing papers per year: 2021: 12; 2022: 11; 2023: 6; 2024: 2.

31 papers.

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  1. 2021Trustworthy AI: From Principles to Practicescited by 206
  2. 2022Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AIcited by 169
  3. 2021Software Engineering for AI-Based Systems: A Surveycited by 68
  4. 2022Investigating Explainability of Generative AI for Code through Scenario-based Designcited by 47
  5. 2024Trust in AI: Progress, Challenges, and Future Directionscited by 35
  6. 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAIcited by 33
  7. 2022Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desideratacited by 28
  8. 2021"Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?cited by 24
  9. 2021Characterizing and Detecting Mismatch in Machine-Learning-Enabled Systemscited by 19
  10. 2022Adaptive Sampling Strategies to Construct Equitable Training Datasetscited by 18
  11. 2023Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"cited by 15
  12. 2022Explainable AI via Learning to Optimizecited by 14
19 more
  1. 2023Auditing and Generating Synthetic Data with Controllable Trust Trade-offscited by 13
  2. 2021Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Modelscited by 12
  3. 2023fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Taskscited by 10
  4. 2021Software Engineering for Responsible AI: An Empirical Study and Operationalised Patternscited by 9
  5. 2023AI Maintenance: A Robustness Perspectivecited by 6
  6. 2021A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributionscited by 5
  7. 2021Know Your Model (KYM): Increasing Trust in AI and Machine Learningcited by 4
  8. 2022Towards an Accountable and Reproducible Federated Learning: A FactSheets Approachcited by 3
  9. 2022Tailored Uncertainty Estimation for Deep Learning Systemscited by 3
  10. 2021TDM: Trustworthy Decision-Making via Interpretability Enhancementcited by 1
  11. 2022Network Report: A Structured Description for Network Datasetscited by 1
  12. 2022Development and Validation of ML-DQA -- a Machine Learning Data Quality Assurance Framework for Healthcarecited by 1
  13. 2022Documenting use cases in the affective computing domain using Unified Modeling Languagecited by 1
  14. 2021The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AIcited by 0
  15. 2021We Need to Talk About Data: The Importance of Data Readiness in Natural Language Processingcited by 0
  16. 2021Validation and Transparency in AI systems for pharmacovigilance: a case study applied to the medical literature monitoring of adverse eventscited by 0
  17. 2022The "Collections as ML Data" Checklist for Machine Learning & Cultural Heritagecited by 0
  18. 2023Fiduciary Responsibility: Facilitating Public Trust in Automated Decision Makingcited by 0
  19. 2024Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworkscited by 0