research-article

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

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S. Shyam SundarAuthors Info & Claims

CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Article No.: 816, Pages 1 - 11

https://doi.org/10.1145/3544548.3580805

Published: 19 April 2023 Publication History

Abstract

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.

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Index Terms

Is this AI trained on Credible Data? The Effects of Labeling Quality and Performance Bias on User Trust
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

April 2023

14911 pages

ISBN:9781450394215

DOI:10.1145/3544548

Editors:
Albrecht Schmidt
LMU Munich, Germany60028717
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Kaisa Väänänen
Tampere University, Finland60011170
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Tesh Goyal
Google Research, USA60006191
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Per Ola Kristensson
University of Cambridge, UK60031101
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Anicia Peters
University of Namibia, Namibia60072704
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Stefanie Mueller
Massachusetts Institute of Technology, USA60022195
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Julie R. Williamson
University of Glasgow, UK60001490
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University of Nottingham, UK60015138

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Giner-Miguelez JGómez ACabot J(2025)On the Readiness of Scientific Data Papers for a Fair and Transparent Use in Machine LearningScientific Data10.1038/s41597-025-04402-412:1Online publication date: 13-Jan-2025
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