Facade of Trust: The Impact of Adopting AI-Enhanced Profile Pictures on Trust

Virtual profiles are critical elements of online social interactions, often featuring portraits enhanced by AI-driven tools such as beauty filters and virtual makeup. While the increasing use of these technologies reshapes how individuals present and perceive themselves, little is known about their implications for trust-related behavior. Drawing on self-presentation theory, this study examines how adopting an enhanced profile photo influences behavioral trust and trustworthiness toward others. We conducted a randomized experiment based on the trust game and adapted it by embedding a novel profile photo enhancement module. Participants were assigned to either a treatment group, in which they adopted an AI-enhanced profile photo, or a control group, in which they used their original photo. Our findings reveal an intriguing asymmetry. Senders with an enhanced photo demonstrated less trust by sending smaller amounts. Conversely, receivers with an enhanced photo displayed greater trustworthiness, returning higher percentages. These effects were offset when participants were interacting with an opponent using an enhanced photo. Further analysis indicates that enhanced self-presentation increases perceived risk, which explains the observed reduction in trust. We also document gender differences: female senders show a larger decline in trust, while male receivers exhibit a greater increase in trustworthiness. Overall, this research highlights the asymmetric behavioral consequences of AI-powered self-presentation and underscores the role of risk perception in linking profile photo enhancement to trust behavior in online interactions.

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Publication Details

Journal
Information Systems Research
Published
2026-10-05
DOI
https://doi.org/10.1287/isre.2023.0431
Primary Topic
Privacy, Security, and Data Protection
Type
article
Field-Weighted Citation Impact
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article

Facade of Trust: The Impact of Adopting AI-Enhanced Profile Pictures on Trust

Tao Lu, Xiaoquan Zhang, Sijia Ma, Chong Wang
Information Systems Research
Privacy, Security, and Data Protection
article

Facade of Trust: The Impact of Adopting AI-Enhanced Profile Pictures on Trust

Tao Lu, Xiaoquan Zhang, Sijia Ma, Chong Wang
article en

Abstract

Virtual profiles are critical elements of online social interactions, often featuring portraits enhanced by AI-driven tools such as beauty filters and virtual makeup. While the increasing use of these technologies reshapes how individuals present and perceive themselves, little is known about their implications for trust-related behavior. Drawing on self-presentation theory, this study examines how adopting an enhanced profile photo influences behavioral trust and trustworthiness toward others. We conducted a randomized experiment based on the trust game and adapted it by embedding a novel profile photo enhancement module. Participants were assigned to either a treatment group, in which they adopted an AI-enhanced profile photo, or a control group, in which they used their original photo. Our findings reveal an intriguing asymmetry. Senders with an enhanced photo demonstrated less trust by sending smaller amounts. Conversely, receivers with an enhanced photo displayed greater trustworthiness, returning higher percentages. These effects were offset when participants were interacting with an opponent using an enhanced photo. Further analysis indicates that enhanced self-presentation increases perceived risk, which explains the observed reduction in trust. We also document gender differences: female senders show a larger decline in trust, while male receivers exhibit a greater increase in trustworthiness. Overall, this research highlights the asymmetric behavioral consequences of AI-powered self-presentation and underscores the role of risk perception in linking profile photo enhancement to trust behavior in online interactions.

Information Systems Research
Chinese University of Hong Kong (HK), Tilburg University (NL), Peking University (CN), Southern University of Science and Technology (CN), Decision Sciences (United States) (US)
Openalex Percentile: Top 5%
Privacy, Security, and Data Protection
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