# Algorithmic Transparency with Strategic Users

arXiv: https://arxiv.org/abs/2008.09283 · HTML: https://mlsanity.com/paper/2008.09283/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2020.

## Built on by (most cited first, 8 of 8)

- [Algorithmic Insurance](https://mlsanity.com/paper/2106.00839/) (2021)
- [Dynamics of Adversarial Attacks on Large Language Model-Based Search Engines](https://mlsanity.com/paper/2501.00745/) (2025)
- [Regulating eXplainable Artificial Intelligence (XAI) May Harm Consumers](https://mlsanity.com/paper/2209.03499/) (2022)
- [Expanding Knowledge Graphs with Humans in the Loop](https://mlsanity.com/paper/2212.05189/) (2022)
- [Anticipating Gaming to Incentivize Improvement: Guiding Agents in (Fair) Strategic Classification](https://mlsanity.com/paper/2505.05594/) (2025)
- [Harmonizing Safety and Speed: A Human-Algorithm Approach to Enhance the FDA's Medical Device Clearance Policy](https://mlsanity.com/paper/2407.11823/) (2024)
- [The Economics of AI Supply Chain Regulation](https://mlsanity.com/paper/2603.12630/) (2026)
- [Robust Strategic Classification under Decision-Dependent Cost Uncertainty](https://mlsanity.com/paper/2606.30136/) (2026)

## Abstract (arXiv)

Should firms that apply machine learning algorithms in their decision-making make their algorithms transparent to the users they affect? Despite growing calls for algorithmic transparency, most firms have kept their algorithms opaque, citing potential gaming by users that may negatively affect the algorithm's predictive power. We develop an analytical model to compare firm and user surplus with and without algorithmic transparency in the presence of strategic users and present novel insights. We identify a broad set of conditions under which making the algorithm transparent benefits the firm. We show that, in some cases, even the predictive power of machine learning algorithms may increase if the firm makes them transparent. By contrast, users may not always be better off under algorithmic transparency. The results hold even when the predictive power of the opaque algorithm comes largely from correlational features and the cost for users to improve on them is close to zero. Overall, our results show that firms should not view manipulation by users as bad. Rather, they should use algorithmic transparency as a lever to motivate users to invest in more desirable features.