# Regulating eXplainable Artificial Intelligence (XAI) May Harm Consumers

arXiv: https://arxiv.org/abs/2209.03499 · HTML: https://mlsanity.com/paper/2209.03499/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 69 references that are in the map.
- First posted on arXiv 7 Sep 2022.

## Builds on (most cited first, 26 of 26)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (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)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Distribution-Free Predictive Inference For Regression](https://mlsanity.com/paper/1604.04173/) (2016)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [Knockoff Nets: Stealing Functionality of Black-Box Models](https://mlsanity.com/paper/1812.02766/) (2018)
- [Explainable Machine Learning in Deployment](https://mlsanity.com/paper/1909.06342/) (2019)
- [Explaining Explanations in AI](https://mlsanity.com/paper/1811.01439/) (2018)
- [What do we need to build explainable AI systems for the medical domain?](https://mlsanity.com/paper/1712.09923/) (2017)
- [Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination](https://mlsanity.com/paper/1408.6491/) (2014)
- [Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation](https://mlsanity.com/paper/1710.06169/) (2017)
- [Model Explanations with Differential Privacy](https://mlsanity.com/paper/2006.09129/) (2020)
- [Global Model Interpretation via Recursive Partitioning](https://mlsanity.com/paper/1802.04253/) (2018)
- [Algorithmic Transparency with Strategic Users](https://mlsanity.com/paper/2008.09283/) (2020)
- [A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop](https://mlsanity.com/paper/1708.01104/) (2017)
- [Interpretable neural networks based on continuous-valued logic and multicriteria decision operators](https://mlsanity.com/paper/1910.02486/) (2019)
- [Explainable Restricted Boltzmann Machines for Collaborative Filtering](https://mlsanity.com/paper/1606.07129/) (2016)

## Built on by (most cited first, 3 of 3)

- [LLMs for Explainable Business Decision-Making: A Reinforcement Learning Fine-Tuning Approach](https://mlsanity.com/paper/2601.04208/) (2025)
- [Towards Accurate and Interpretable Time-series Forecasting: A Polynomial Learning Approach](https://mlsanity.com/paper/2603.02906/) (2026)
- [The Economics of AI Supply Chain Regulation](https://mlsanity.com/paper/2603.12630/) (2026)

## Abstract (arXiv)

Recent AI algorithms are black box models whose decisions are difficult to interpret. eXplainable AI (XAI) is a class of methods that seek to address lack of AI interpretability and trust by explaining to customers their AI decisions. The common wisdom is that regulating AI by mandating fully transparent XAI leads to greater social welfare. Our paper challenges this notion through a game theoretic model of a policy-maker who maximizes social welfare, firms in a duopoly competition that maximize profits, and heterogenous consumers. The results show that XAI regulation may be redundant. In fact, mandating fully transparent XAI may make firms and consumers worse off. This reveals a tradeoff between maximizing welfare and receiving explainable AI outputs. We extend the existing literature on method and substantive fronts, and we introduce and study the notion of XAI fairness, which may be impossible to guarantee even under mandatory XAI. Finally, the regulatory and managerial implications of our results for policy-makers and businesses are discussed, respectively.