# What Are You Hiding? Algorithmic Transparency and User Perceptions

arXiv: https://arxiv.org/abs/1812.03220 · HTML: https://mlsanity.com/paper/1812.03220/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2018.

## Built on by (most cited first, 1 of 1)

- [Users are the North Star for AI Transparency](https://mlsanity.com/paper/2303.05500/) (2023)

## Abstract (arXiv)

Extensive recent media focus has been directed towards the dark side of intelligent systems, how algorithms can influence society negatively. Often, transparency is proposed as a solution or step in the right direction. Unfortunately, research is mixed on the impact of transparency on the user experience. We examine transparency in the context an interactive system that predicts positive/negative emotion from a users' written text. We unify seemingly this contradictory research under a single model. We show that transparency can negatively affect accuracy perceptions for users whose expectations were not violated by the system's prediction; however, transparency also limits the damage done when users' expectations are violated by system predictions.