mlsanityData to 7 Oct 2026

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

arXiv 2106.11036 ↗First posted 31 May 2021cs.CY cs.AI cs.LGcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 20 of its 62 references that are in the map. First posted on arXiv 31 May 2021.

Builds on

20 of 62 references.

References · 20

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8 more
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Cited by year

Citing papers per year: 2024: 1; 2025: 3.

4 papers.

  1. 2024Trust in AI: Progress, Challenges, and Future Directionscited by 35
  2. 2025Is Trust Correlated With Explainability in AI? A Meta-Analysiscited by 7
  3. 2025A2Perf: Real-World Autonomous Agents Benchmarkcited by 2
  4. 2025Preliminary Quantitative Study on Explainability and Trust in AI Systemscited by 0