Selective Trust: Understanding Human-AI Partnerships in Personal Health Decision-Making Process

Sterre Van Arum; Hüseyin Uğur Genç; Dennis Reidsma; Armağan Karahanoğlu

doi:10.1145/3706598.3713462

Selective Trust: Understanding Human-AI Partnerships in Personal Health Decision-Making Process

Sterre Van Arum
, Hüseyin Uğur Genç
, Dennis Reidsma
, Armağan Karahanoğlu

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

14 Citations (Scopus)

36 Downloads (Pure)

Abstract

As artificial intelligence (AI) becomes more embedded in personal health technology, its potential to transform health decision-making through personalised recommendations is becoming significant. However, there is limited understanding of how individuals perceive AI-assisted decision-making in the context of personal health. This study investigates the impact of AI-assisted decision-making on trust in physical activity-related health decisions. By employing MoveAI, a GPT-4.0-based physical activity decision-making tool, we conducted a mixed-methods study and conducted an online survey (N=184) and semi-structured interviews (N=24) to explore this dynamic. Our findings emphasise the role of nuanced personal health recommendations and individual decision-making styles in shaping trust in AI-assisted personal health decision-making. This paper contributes to the HCI literature by elucidating the relationship between decision-making styles and trust in the AI-assisted personal health decision-making process and showing the challenges of aligning AI recommendations with individual decision-making preferences.

Original language	English
Title of host publication	CHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
Editors	N. Yamashita, V. Evers, K. Yatani, X. Ding, B. Lee, M. Chetty, P. Toups-Dugas
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Number of pages	21
ISBN (Print)	979-8-4007-1394-1
DOIs	https://doi.org/10.1145/3706598.3713462
Publication status	Published - 2025
Event	2025 CHI Conference on Human Factors in Computing Systems, CHI 2025 - Yokohama, Japan Duration: 26 Apr 2025 → 1 May 2025

Conference

Conference	2025 CHI Conference on Human Factors in Computing Systems, CHI 2025
Abbreviated title	CHI
Country/Territory	Japan
City	Yokohama
Period	26/04/25 → 1/05/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

AI-assisted decision-making
decision-making styles
personal health technology
physical activity decisions
trust

Access to Document

10.1145/3706598.3713462

3706598.3713462Final published version, 687 KBLicence: CC BY-NC

Cite this

Van Arum, S., Genç, H. U., Reidsma, D., & Karahanoğlu, A. (2025). Selective Trust: Understanding Human-AI Partnerships in Personal Health Decision-Making Process. In N. Yamashita, V. Evers, K. Yatani, X. Ding, B. Lee, M. Chetty, & P. Toups-Dugas (Eds.), CHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems Article 1026 Association for Computing Machinery (ACM). https://doi.org/10.1145/3706598.3713462

Van Arum, Sterre ; Genç, Hüseyin Uğur ; Reidsma, Dennis et al. / Selective Trust : Understanding Human-AI Partnerships in Personal Health Decision-Making Process. CHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. editor / N. Yamashita ; V. Evers ; K. Yatani ; X. Ding ; B. Lee ; M. Chetty ; P. Toups-Dugas. New York : Association for Computing Machinery (ACM), 2025.

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abstract = "As artificial intelligence (AI) becomes more embedded in personal health technology, its potential to transform health decision-making through personalised recommendations is becoming significant. However, there is limited understanding of how individuals perceive AI-assisted decision-making in the context of personal health. This study investigates the impact of AI-assisted decision-making on trust in physical activity-related health decisions. By employing MoveAI, a GPT-4.0-based physical activity decision-making tool, we conducted a mixed-methods study and conducted an online survey (N=184) and semi-structured interviews (N=24) to explore this dynamic. Our findings emphasise the role of nuanced personal health recommendations and individual decision-making styles in shaping trust in AI-assisted personal health decision-making. This paper contributes to the HCI literature by elucidating the relationship between decision-making styles and trust in the AI-assisted personal health decision-making process and showing the challenges of aligning AI recommendations with individual decision-making preferences.",

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author = "\{Van Arum\}, Sterre and Gen{\c c}, \{H{\"u}seyin Uğur\} and Dennis Reidsma and Armağan Karahanoğlu",

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doi = "10.1145/3706598.3713462",

language = "English",

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editor = "N. Yamashita and V. Evers and K. Yatani and X. Ding and B. Lee and M. Chetty and P. Toups-Dugas",

booktitle = "CHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems",

publisher = "Association for Computing Machinery (ACM)",

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Van Arum, S, Genç, HU, Reidsma, D & Karahanoğlu, A 2025, Selective Trust: Understanding Human-AI Partnerships in Personal Health Decision-Making Process. in N Yamashita, V Evers, K Yatani, X Ding, B Lee, M Chetty & P Toups-Dugas (eds), CHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems., 1026, Association for Computing Machinery (ACM), New York, 2025 CHI Conference on Human Factors in Computing Systems, CHI 2025, Yokohama, Japan, 26/04/25. https://doi.org/10.1145/3706598.3713462

Selective Trust: Understanding Human-AI Partnerships in Personal Health Decision-Making Process. / Van Arum, Sterre; Genç, Hüseyin Uğur; Reidsma, Dennis et al.
CHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. ed. / N. Yamashita; V. Evers; K. Yatani; X. Ding; B. Lee; M. Chetty; P. Toups-Dugas. New York: Association for Computing Machinery (ACM), 2025. 1026.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Selective Trust

T2 - 2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

AU - Van Arum, Sterre

AU - Genç, Hüseyin Uğur

AU - Reidsma, Dennis

AU - Karahanoğlu, Armağan

PY - 2025

Y1 - 2025

N2 - As artificial intelligence (AI) becomes more embedded in personal health technology, its potential to transform health decision-making through personalised recommendations is becoming significant. However, there is limited understanding of how individuals perceive AI-assisted decision-making in the context of personal health. This study investigates the impact of AI-assisted decision-making on trust in physical activity-related health decisions. By employing MoveAI, a GPT-4.0-based physical activity decision-making tool, we conducted a mixed-methods study and conducted an online survey (N=184) and semi-structured interviews (N=24) to explore this dynamic. Our findings emphasise the role of nuanced personal health recommendations and individual decision-making styles in shaping trust in AI-assisted personal health decision-making. This paper contributes to the HCI literature by elucidating the relationship between decision-making styles and trust in the AI-assisted personal health decision-making process and showing the challenges of aligning AI recommendations with individual decision-making preferences.

AB - As artificial intelligence (AI) becomes more embedded in personal health technology, its potential to transform health decision-making through personalised recommendations is becoming significant. However, there is limited understanding of how individuals perceive AI-assisted decision-making in the context of personal health. This study investigates the impact of AI-assisted decision-making on trust in physical activity-related health decisions. By employing MoveAI, a GPT-4.0-based physical activity decision-making tool, we conducted a mixed-methods study and conducted an online survey (N=184) and semi-structured interviews (N=24) to explore this dynamic. Our findings emphasise the role of nuanced personal health recommendations and individual decision-making styles in shaping trust in AI-assisted personal health decision-making. This paper contributes to the HCI literature by elucidating the relationship between decision-making styles and trust in the AI-assisted personal health decision-making process and showing the challenges of aligning AI recommendations with individual decision-making preferences.

KW - AI-assisted decision-making

KW - decision-making styles

KW - personal health technology

KW - physical activity decisions

KW - trust

UR - https://www.scopus.com/pages/publications/105005751123

U2 - 10.1145/3706598.3713462

DO - 10.1145/3706598.3713462

M3 - Conference contribution

AN - SCOPUS:105005751123

SN - 979-8-4007-1394-1

BT - CHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

A2 - Yamashita, N.

A2 - Evers, V.

A2 - Yatani, K.

A2 - Ding, X.

A2 - Lee, B.

A2 - Chetty, M.

A2 - Toups-Dugas, P.

PB - Association for Computing Machinery (ACM)

CY - New York

Y2 - 26 April 2025 through 1 May 2025

ER -

Van Arum S, Genç HU, Reidsma D, Karahanoğlu A. Selective Trust: Understanding Human-AI Partnerships in Personal Health Decision-Making Process. In Yamashita N, Evers V, Yatani K, Ding X, Lee B, Chetty M, Toups-Dugas P, editors, CHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. New York: Association for Computing Machinery (ACM). 2025. 1026 doi: 10.1145/3706598.3713462

Selective Trust: Understanding Human-AI Partnerships in Personal Health Decision-Making Process

Abstract

Conference

UN SDGs

Keywords

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