# Know Your Model (KYM): Increasing Trust in AI and Machine Learning

arXiv: https://arxiv.org/abs/2106.11036 · HTML: https://mlsanity.com/paper/2106.11036/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 62 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 20 of 20)

- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Adversarial Robustness Toolbox v1.0.0](https://mlsanity.com/paper/1807.01069/) (2018)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches](https://mlsanity.com/paper/1912.05100/) (2019)
- [The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards](https://mlsanity.com/paper/1805.03677/) (2018)
- [Differential Privacy and Machine Learning: a Survey and Review](https://mlsanity.com/paper/1412.7584/) (2014)
- [On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products](https://mlsanity.com/paper/1610.01256/) (2016)
- [Transparency and Explanation in Deep Reinforcement Learning Neural Networks](https://mlsanity.com/paper/1809.06061/) (2018)
- [FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity](https://mlsanity.com/paper/1808.07261/) (2018)
- [Tutorial: Safe and Reliable Machine Learning](https://mlsanity.com/paper/1904.07204/) (2019)
- [Experiences with Improving the Transparency of AI Models and Services](https://mlsanity.com/paper/1911.08293/) (2019)
- [Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering](https://mlsanity.com/paper/1910.04223/) (2019)
- [The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey](https://mlsanity.com/paper/1911.02621/) (2019)
- [Asset Management in Machine Learning: A Survey](https://mlsanity.com/paper/2102.06919/) (2021)
- [From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices](https://mlsanity.com/paper/1905.06876/) (2019)

## Built on by (most cited first, 4 of 4)

- [Trust in AI: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2403.14680/) (2024)
- [Is Trust Correlated With Explainability in AI? A Meta-Analysis](https://mlsanity.com/paper/2504.12529/) (2025)
- [A2Perf: Real-World Autonomous Agents Benchmark](https://mlsanity.com/paper/2503.03056/) (2025)
- [Preliminary Quantitative Study on Explainability and Trust in AI Systems](https://mlsanity.com/paper/2510.15769/) (2025)

## Abstract (arXiv)

The widespread utilization of AI systems has drawn attention to the potential impacts of such systems on society. Of particular concern are the consequences that prediction errors may have on real-world scenarios, and the trust humanity places in AI systems. It is necessary to understand how we can evaluate trustworthiness in AI and how individuals and entities alike can develop trustworthy AI systems. In this paper, we analyze each element of trustworthiness and provide a set of 20 guidelines that can be leveraged to ensure optimal AI functionality while taking into account the greater ethical, technical, and practical impacts to humanity. Moreover, the guidelines help ensure that trustworthiness is provable and can be demonstrated, they are implementation agnostic, and they can be applied to any AI system in any sector.