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

arXiv: https://arxiv.org/abs/1808.07261 · HTML: https://mlsanity.com/paper/1808.07261/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2018.

## Built on by (most cited first, 30 of 31)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
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- [Trust in AI: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2403.14680/) (2024)
- [Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI](https://mlsanity.com/paper/2302.00799/) (2023)
- [Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata](https://mlsanity.com/paper/2206.02923/) (2022)
- ["Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?](https://mlsanity.com/paper/2107.02278/) (2021)
- [Characterizing and Detecting Mismatch in Machine-Learning-Enabled Systems](https://mlsanity.com/paper/2103.14101/) (2021)
- [Adaptive Sampling Strategies to Construct Equitable Training Datasets](https://mlsanity.com/paper/2202.01327/) (2022)
- [Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"](https://mlsanity.com/paper/2302.01448/) (2023)
- [Explainable AI via Learning to Optimize](https://mlsanity.com/paper/2204.14174/) (2022)
- [Auditing and Generating Synthetic Data with Controllable Trust Trade-offs](https://mlsanity.com/paper/2304.10819/) (2023)
- [Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models](https://mlsanity.com/paper/2102.00036/) (2021)
- [fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks](https://mlsanity.com/paper/2302.11703/) (2023)
- [Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns](https://mlsanity.com/paper/2111.09478/) (2021)
- [AI Maintenance: A Robustness Perspective](https://mlsanity.com/paper/2301.03052/) (2023)
- [A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributions](https://mlsanity.com/paper/2103.03610/) (2021)
- [Know Your Model (KYM): Increasing Trust in AI and Machine Learning](https://mlsanity.com/paper/2106.11036/) (2021)
- [Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach](https://mlsanity.com/paper/2202.12443/) (2022)
- [Tailored Uncertainty Estimation for Deep Learning Systems](https://mlsanity.com/paper/2204.13963/) (2022)
- [TDM: Trustworthy Decision-Making via Interpretability Enhancement](https://mlsanity.com/paper/2108.06080/) (2021)
- [Network Report: A Structured Description for Network Datasets](https://mlsanity.com/paper/2206.03635/) (2022)
- [Development and Validation of ML-DQA -- a Machine Learning Data Quality Assurance Framework for Healthcare](https://mlsanity.com/paper/2208.02670/) (2022)
- [Documenting use cases in the affective computing domain using Unified Modeling Language](https://mlsanity.com/paper/2209.09666/) (2022)
- [The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI](https://mlsanity.com/paper/2107.14052/) (2021)
- [We Need to Talk About Data: The Importance of Data Readiness in Natural Language Processing](https://mlsanity.com/paper/2110.05464/) (2021)
- [Validation and Transparency in AI systems for pharmacovigilance: a case study applied to the medical literature monitoring of adverse events](https://mlsanity.com/paper/2201.00692/) (2021)
- [The "Collections as ML Data" Checklist for Machine Learning & Cultural Heritage](https://mlsanity.com/paper/2207.02960/) (2022)
- [Fiduciary Responsibility: Facilitating Public Trust in Automated Decision Making](https://mlsanity.com/paper/2301.10001/) (2023)

## 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.