"Trust Junk" Leads to Unjustified Support for Highly Discriminatory Predictive Models
The persuasive power of data visualizations can go awry: for instance, in an explainable AI (XAI) context, visualizations can produce over-trust of predictive models. In this paper, we use a crowdsourced study to show that providing accurate (but superfluous or irrelevant) data in a model explanation can, in fact, result in unjustified trust and other positive beliefs about a model, even when the model is patently discriminatory and unfair. Our results suggest that XAI designers and developers n
Record details
Published: 14 July 2026
Source: arXiv cs.HC
Category: Research
Topics: Bias & fairness · Transparency
Retrieved: 18 July 2026
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How to cite this record
ethics.ai (14 July 2026), “"Trust Junk" Leads to Unjustified Support for Highly Discriminatory Predictive Models,” evidence record 11623, https://ethics.ai/record/11623 (originally published by arXiv cs.HC).
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