# Transparency: Motivations and Challenges

arXiv: https://arxiv.org/abs/1708.01870 · HTML: https://mlsanity.com/paper/1708.01870/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2017.

## Built on by (most cited first, 10 of 10)

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- [Tensions Between the Proxies of Human Values in AI](https://mlsanity.com/paper/2212.07508/) (2022)
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- [Seeing What Shouldn't Be There: Counterfactual GANs for Medical Image Attribution](https://mlsanity.com/paper/2605.05283/) (2026)
- ["Trust Junk" Leads to Unjustified Support for Highly Discriminatory Predictive Models](https://mlsanity.com/paper/2607.14152/) (2026)

## Abstract (arXiv)

Transparency is often deemed critical to enable effective real-world deployment of intelligent systems. Yet the motivations for and benefits of different types of transparency can vary significantly depending on context, and objective measurement criteria are difficult to identify. We provide a brief survey, suggesting challenges and related concerns. We highlight and review settings where transparency may cause harm, discussing connections across privacy, multi-agent game theory, economics, fairness and trust.