# Machine Learning Explainability for External Stakeholders

arXiv: https://arxiv.org/abs/2007.05408 · HTML: https://mlsanity.com/paper/2007.05408/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 17 of 17)

- [Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations](https://mlsanity.com/paper/2205.07277/) (2022)
- [Robust Explanation Constraints for Neural Networks](https://mlsanity.com/paper/2212.08507/) (2022)
- [What will it take to generate fairness-preserving explanations?](https://mlsanity.com/paper/2106.13346/) (2021)
- [Perspectives on Incorporating Expert Feedback into Model Updates](https://mlsanity.com/paper/2205.06905/) (2022)
- [AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks](https://mlsanity.com/paper/2102.04255/) (2021)
- [Axes for Sociotechnical Inquiry in AI Research](https://mlsanity.com/paper/2105.06551/) (2021)
- [Users are the North Star for AI Transparency](https://mlsanity.com/paper/2303.05500/) (2023)
- [Seven challenges for harmonizing explainability requirements](https://mlsanity.com/paper/2108.05390/) (2021)
- ["Explanation" is Not a Technical Term: The Problem of Ambiguity in XAI](https://mlsanity.com/paper/2207.00007/) (2022)
- [FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines](https://mlsanity.com/paper/2307.15475/) (2023)
- [Explainability in Machine Learning: a Pedagogical Perspective](https://mlsanity.com/paper/2202.10335/) (2022)
- [Holistic Explainable AI (H-XAI): Extending Transparency Beyond Developers in AI-Driven Decision Making](https://mlsanity.com/paper/2508.05792/) (2025)
- [Scalable Explainability-as-a-Service (XaaS) for Edge AI Systems](https://mlsanity.com/paper/2602.04120/) (2026)
- [Do not explain without context: addressing the blind spot of model explanations](https://mlsanity.com/paper/2105.13787/) (2021)
- [Five policy uses of algorithmic transparency and explainability](https://mlsanity.com/paper/2302.03080/) (2023)
- [Clash of the Explainers: Argumentation for Context-Appropriate Explanations](https://mlsanity.com/paper/2312.07635/) (2023)
- [Exploring CoCo Challenges in ML Engineering Teams: Insights From the Semiconductor Industry](https://mlsanity.com/paper/2605.07389/) (2026)

## Abstract (arXiv)

As machine learning is increasingly deployed in high-stakes contexts affecting people's livelihoods, there have been growing calls to open the black box and to make machine learning algorithms more explainable. Providing useful explanations requires careful consideration of the needs of stakeholders, including end-users, regulators, and domain experts. Despite this need, little work has been done to facilitate inter-stakeholder conversation around explainable machine learning. To help address this gap, we conducted a closed-door, day-long workshop between academics, industry experts, legal scholars, and policymakers to develop a shared language around explainability and to understand the current shortcomings of and potential solutions for deploying explainable machine learning in service of transparency goals. We also asked participants to share case studies in deploying explainable machine learning at scale. In this paper, we provide a short summary of various case studies of explainable machine learning, lessons from those studies, and discuss open challenges.