Is this AI trained on Credible Data? The Effects of Labeling Quality and Performance Bias on User Trust

Cheng Chen, S. Shyam Sundar

Research output: Chapter in Book/Report/Conference proceedingConference contribution


To promote data transparency, frameworks such as CrowdWorkSheets encourage documentation of annotation practices on the interfaces of AI systems, but we do not know how they affect user experience. Will the quality of labeling affect perceived credibility of training data? Does the source of annotation matter? Will a credible dataset persuade users to trust a system even if it shows racial biases in its predictions? To find out, we conducted a user study (N = 430) with a prototype of a classification system, using a 2 (labeling quality: high vs. low) × 4 (source: others-as-source vs. self-as-source cue vs. self-as-source voluntary action, vs. self-as-source forced action) × 3 (AI performance: none vs. biased vs. unbiased) experiment. We found that high-quality labeling leads to higher perceived training data credibility, which in turn enhances users' trust in AI, but not when the system shows bias. Practical implications for explainable and ethical AI interfaces are discussed.

Original languageEnglish (US)
Title of host publicationCHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450394215
StatePublished - Apr 19 2023
Event2023 CHI Conference on Human Factors in Computing Systems, CHI 2023 - Hamburg, Germany
Duration: Apr 23 2023Apr 28 2023

Publication series

NameConference on Human Factors in Computing Systems - Proceedings


Conference2023 CHI Conference on Human Factors in Computing Systems, CHI 2023

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Software

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