# Dice in the Black Box: User Experiences with an Inscrutable Algorithm

arXiv: https://arxiv.org/abs/1812.03219 · HTML: https://mlsanity.com/paper/1812.03219/

## Key facts

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

## Built on by (most cited first, 7 of 7)

- [Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset](https://mlsanity.com/paper/2204.11642/) (2022)
- [AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems](https://mlsanity.com/paper/2402.06287/) (2024)
- [Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretability](https://mlsanity.com/paper/2109.11849/) (2021)
- [Painting the black box white: experimental findings from applying XAI to an ECG reading setting](https://mlsanity.com/paper/2210.15236/) (2022)
- [Responding to Generative AI Technologies with Research-through-Design: The Ryelands AI Lab as an Exploratory Study](https://mlsanity.com/paper/2405.04677/) (2024)
- [Robustness and Usefulness in AI Explanation Methods](https://mlsanity.com/paper/2203.03729/) (2022)
- [Policy alone is probably not the solution: A large-scale experiment on how developers struggle to design meaningful end-user explanations](https://mlsanity.com/paper/2503.15512/) (2025)

## Abstract (arXiv)

We demonstrate that users may be prone to place an inordinate amount of trust in black box algorithms that are framed as intelligent. We deploy an algorithm that purportedly assesses the positivity and negativity of a users' writing emotional writing. In actuality, the algorithm responds in a random fashion. We qualitatively examine the paths to trust that users followed while testing the system. In light of the ease with which users may trust systems exhibiting "intelligent behavior" we recommend corrective approaches.