"I had a solid theory before but it's falling apart": Polarizing Effects of Algorithmic Transparency
This research addresses the problem of designing transparent AI systems for users, highlighting that transparency can have polarizing effects, which is incremental as it builds on existing calls for explainability.
The study investigated how algorithmic transparency affects user perceptions of an emotion detection system, finding that transparency improved accuracy perceptions for users without a prior mental model but reduced confidence for those who already had expectations, due to a mismatch between user expectations and system predictions.
The rise of machine learning has brought closer scrutiny to intelligent systems, leading to calls for greater transparency and explainable algorithms. We explore the effects of transparency on user perceptions of a working intelligent system for emotion detection. In exploratory Study 1, we observed paradoxical effects of transparency which improves perceptions of system accuracy for some participants while reducing accuracy perceptions for others. In Study 2, we test this observation using mixed methods, showing that the apparent transparency paradox can be explained by a mismatch between participant expectations and system predictions. We qualitatively examine this process, indicating that transparency can undermine user confidence by causing users to fixate on flaws when they already have a model of system operation. In contrast transparency helps if users lack such a model. Finally, we revisit the notion of transparency and suggest design considerations for building safe and successful machine learning systems based on our insights.