Dice in the Black Box: User Experiences with an Inscrutable Algorithm
Not on the map. Why?
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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Dec 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 2; 2025: 1.
7 papers.
- 2022Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Datasetcited by 13
- 2024AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systemscited by 7
- 2021Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretabilitycited by 4
- 2022Painting the black box white: experimental findings from applying XAI to an ECG reading settingcited by 4
- 2024Responding to Generative AI Technologies with Research-through-Design: The Ryelands AI Lab as an Exploratory Studycited by 2
- 2022Robustness and Usefulness in AI Explanation Methodscited by 0
- 2025Policy alone is probably not the solution: A large-scale experiment on how developers struggle to design meaningful end-user explanationscited by 0
- 2025Policy alone is probably not the solution: A large-scale experiment on how developers struggle to design meaningful end-user explanations28 Jan 2025
- 2024Responding to Generative AI Technologies with Research-through-Design: The Ryelands AI Lab as an Exploratory Study7 May 2024
- 2024AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems9 Feb 2024
- 2022Painting the black box white: experimental findings from applying XAI to an ECG reading setting27 Oct 2022
- 2022Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset25 Apr 2022
- 2022Robustness and Usefulness in AI Explanation Methods7 Mar 2022
- 2021Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretability24 Sep 2021