# Users are the North Star for AI Transparency

arXiv: https://arxiv.org/abs/2303.05500 · HTML: https://mlsanity.com/paper/2303.05500/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 133 references that are in the map.
- First posted on arXiv 9 Mar 2023.

## Builds on (most cited first, 30 of 39)

- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [Attention is not not Explanation](https://mlsanity.com/paper/1908.04626/) (2019)
- [Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?](https://mlsanity.com/paper/2004.03685/) (2020)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://mlsanity.com/paper/1906.02361/) (2019)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [Generating Visual Explanations](https://mlsanity.com/paper/1603.08507/) (2016)
- [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020)
- [Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty](https://mlsanity.com/paper/2011.07586/) (2020)
- [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021)
- [Troubling Trends in Machine Learning Scholarship](https://mlsanity.com/paper/1807.03341/) (2018)
- [Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions](https://mlsanity.com/paper/1901.03729/) (2019)
- [The relationship between trust in AI and trustworthy machine learning technologies](https://mlsanity.com/paper/1912.00782/) (2019)
- [What Does Explainable AI Really Mean? A New Conceptualization of Perspectives](https://mlsanity.com/paper/1710.00794/) (2017)
- [Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making](https://mlsanity.com/paper/1802.01029/) (2018)
- [Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation](https://mlsanity.com/paper/1710.06169/) (2017)
- [Accurate, reliable and fast robustness evaluation](https://mlsanity.com/paper/1907.01003/) (2019)
- [Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences](https://mlsanity.com/paper/2001.09604/) (2020)
- [Human-Centered Artificial Intelligence and Machine Learning](https://mlsanity.com/paper/1901.11184/) (2019)
- [Privacy Policies over Time: Curation and Analysis of a Million-Document Dataset](https://mlsanity.com/paper/2008.09159/) (2020)
- [Machine Learning Explainability for External Stakeholders](https://mlsanity.com/paper/2007.05408/) (2020)
- [Experiences with Improving the Transparency of AI Models and Services](https://mlsanity.com/paper/1911.08293/) (2019)
- [Mitigating Covertly Unsafe Text within Natural Language Systems](https://mlsanity.com/paper/2210.09306/) (2022)
- [On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness, and Semantic Evaluation](https://mlsanity.com/paper/2106.04753/) (2021)
- [Transparency: Motivations and Challenges](https://mlsanity.com/paper/1708.01870/) (2017)

## Built on by (most cited first, 5 of 5)

- [Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text](https://mlsanity.com/paper/2304.06939/) (2023)
- [Explaining Groups of Instances Counterfactually for XAI: A Use Case, Algorithm and User Study for Group-Counterfactuals](https://mlsanity.com/paper/2303.09297/) (2023)
- [A Co-design Study for Multi-Stakeholder Job Recommender System Explanations](https://mlsanity.com/paper/2309.05507/) (2023)
- [Towards User-Focused Research in Training Data Attribution for Human-Centered Explainable AI](https://mlsanity.com/paper/2409.16978/) (2024)
- [Confident Teacher, Confident Student? A Novel User Study Design for Investigating the Didactic Potential of Explanations and their Impact on Uncertainty](https://mlsanity.com/paper/2409.17157/) (2024)

## Abstract (arXiv)

Despite widespread calls for transparent artificial intelligence systems, the term is too overburdened with disparate meanings to express precise policy aims or to orient concrete lines of research. Consequently, stakeholders often talk past each other, with policymakers expressing vague demands and practitioners devising solutions that may not address the underlying concerns. Part of why this happens is that a clear ideal of AI transparency goes unsaid in this body of work. We explicitly name such a north star -- transparency that is user-centered, user-appropriate, and honest. We conduct a broad literature survey, identifying many clusters of similar conceptions of transparency, tying each back to our north star with analysis of how it furthers or hinders our ideal AI transparency goals. We conclude with a discussion on common threads across all the clusters, to provide clearer common language whereby policymakers, stakeholders, and practitioners can communicate concrete demands and deliver appropriate solutions. We hope for future work on AI transparency that further advances confident, user-beneficial goals and provides clarity to regulators and developers alike.