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How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface

Author:

René F. KizilcecAuthors Info & Claims

CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

Pages 2390 - 2395

https://doi.org/10.1145/2858036.2858402

Published: 07 May 2016 Publication History

Abstract

The rising prevalence of algorithmic interfaces, such as curated feeds in online news, raises new questions for designers, scholars, and critics of media. This work focuses on how transparent design of algorithmic interfaces can promote awareness and foster trust. A two-stage process of how transparency affects trust was hypothesized drawing on theories of information processing and procedural justice. In an online field experiment, three levels of system transparency were tested in the high-stakes context of peer assessment. Individuals whose expectations were violated (by receiving a lower grade than expected) trusted the system less, unless the grading algorithm was made more transparent through explanation. However, providing too much information eroded this trust. Attitudes of individuals whose expectations were met did not vary with transparency. Results are discussed in terms of a dual process model of attitude change and the depth of justification of perceived inconsistency. Designing for trust requires balanced interface transparency - not too little and not too much.

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Wischnewski MKrämer NJaniesch CMüller ESchnitzler TNewen C(2024)In Seal We Trust? Investigating the Effect of Certifications on Perceived Trustworthiness of AI SystemsHuman-Machine Communication10.30658/hmc.8.78(141-162)Online publication date: 2024
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Henning ALangenbach P(2024)Bridging the Human-Automation Fairness Gap: How Providing Reasons Enhances the Perceived Fairness of Public Decision-MakingSSRN Electronic Journal10.2139/ssrn.4819145Online publication date: 2024
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Index Terms

How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface
1. Applied computing
  1. Education
2. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Conferences

CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

May 2016

6108 pages

ISBN:9781450333627

DOI:10.1145/2858036

General Chairs:
Jofish Kaye
Yahoo
,
Allison Druin
University of Maryland / National Park Service
,
Program Chairs:
Cliff Lampe
University of Michigan
,
Dan Morris
Microsoft
,
Juan Pablo Hourcade
University of Iowa

Copyright © 2016 ACM.

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Association for Computing Machinery

New York, NY, United States

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Published: 07 May 2016

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CHI'16

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CHI'16: CHI Conference on Human Factors in Computing Systems

May 7 - 12, 2016

California, San Jose, USA

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CHI '16 Paper Acceptance Rate 565 of 2,435 submissions, 23%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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https://doi.org/10.3390/su16020781
Wischnewski MKrämer NJaniesch CMüller ESchnitzler TNewen C(2024)In Seal We Trust? Investigating the Effect of Certifications on Perceived Trustworthiness of AI SystemsHuman-Machine Communication10.30658/hmc.8.78(141-162)Online publication date: 2024
https://doi.org/10.30658/hmc.8.7
Henning ALangenbach P(2024)Bridging the Human-Automation Fairness Gap: How Providing Reasons Enhances the Perceived Fairness of Public Decision-MakingSSRN Electronic Journal10.2139/ssrn.4819145Online publication date: 2024
https://doi.org/10.2139/ssrn.4819145
Matzen LGastelum ZHowell BDivis KStites M(2024)Effects of machine learning errors on human decision-making: manipulations of model accuracy, error types, and error importanceCognitive Research: Principles and Implications10.1186/s41235-024-00586-29:1Online publication date: 26-Aug-2024
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