Do People Appropriately Rely on AI-Advice? An Analytical Review of HCI Research on Human-AI Decision-Making

Muhammad Raees; Vassilis Javed Khan; Ioanna Lykourentzou; Konstantinos Papangelis

doi:10.1145/3772318.3791467

Do People Appropriately Rely on AI-Advice? An Analytical Review of HCI Research on Human-AI Decision-Making

Muhammad Raees^*
, Vassilis Javed Khan
, Ioanna Lykourentzou
, Konstantinos Papangelis

^*Corresponding author for this work

Sub Software Technology for Learning and Teaching

Rochester Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

AI systems are increasingly being positioned to assist people in decision-making. However, recent empirical studies show critical concerns that people over-rely on AI advice without analytically engaging with it. While HCI research explores how people rely on AI advice, we argue that it largely overlooks an important aspect: replicating realistic decision-making scenarios. Human-AI interaction factors influence people's reliance on AI advice. To understand human-AI interaction factors and their interplay, we conducted an analytical review of recent studies in human-AI reliance literature. We analyzed the decision-making tasks in research and their validity in application-grounded contexts. Our findings show that user engagement is a precious commodity for relying on AI advice; however, it comes at a cost. We also discuss factors contributing to "appropriate reliance", existing research gaps, and recommendations for intervention design for human-AI reliance. Our work contributes to the critical body of research on building appropriate reliance on AI advice.

Original language	English
Title of host publication	CHI 2026 - Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
Editors	Nuria Oliver, David A. Shamma, Heloisa Candello, Pablo Cesar, Pedro Lopes, Alessandro Bozzon, Thomas Kosch, Vera Liao, Xiaojuan Ma, Valentino Artizzu, Fiona Draxler, Gustavo Lopez, Anke V. Reinschluessel, Xin Tong, Phoebe O. Toups Dugas
Publisher	Association for Computing Machinery
ISBN (Print)	979-8-4007-2278-3
DOIs	https://doi.org/10.1145/3772318.3791467
Publication status	Published - 13 Apr 2026
Event	2026 CHI Conference on Human Factors in Computing Systems, CHI 2026 - Barcelona, Spain Duration: 13 Apr 2026 → 17 Apr 2026

Conference

Conference	2026 CHI Conference on Human Factors in Computing Systems, CHI 2026
Country/Territory	Spain
City	Barcelona
Period	13/04/26 → 17/04/26

Bibliographical note

Publisher Copyright:
© 2026 Copyright held by the owner/author(s).

Keywords

AI Reliance
Appropriate Reliance
Human-AI Decision Making
Human-AI Reliance

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10.1145/3772318.3791467Licence: CC BY

Do People Appropriately Rely on AI-Advice? An Analytical Review of HCI Research on Human-AI Decision-MakingFinal published version, 1.37 MBLicence: CC BY

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Raees, M., Khan, V. J., Lykourentzou, I., & Papangelis, K. (2026). Do People Appropriately Rely on AI-Advice? An Analytical Review of HCI Research on Human-AI Decision-Making. In N. Oliver, D. A. Shamma, H. Candello, P. Cesar, P. Lopes, A. Bozzon, T. Kosch, V. Liao, X. Ma, V. Artizzu, F. Draxler, G. Lopez, A. V. Reinschluessel, X. Tong, & P. O. Toups Dugas (Eds.), CHI 2026 - Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems Article 805 Association for Computing Machinery. https://doi.org/10.1145/3772318.3791467

Raees, Muhammad ; Khan, Vassilis Javed ; Lykourentzou, Ioanna et al. / Do People Appropriately Rely on AI-Advice? An Analytical Review of HCI Research on Human-AI Decision-Making. CHI 2026 - Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. editor / Nuria Oliver ; David A. Shamma ; Heloisa Candello ; Pablo Cesar ; Pedro Lopes ; Alessandro Bozzon ; Thomas Kosch ; Vera Liao ; Xiaojuan Ma ; Valentino Artizzu ; Fiona Draxler ; Gustavo Lopez ; Anke V. Reinschluessel ; Xin Tong ; Phoebe O. Toups Dugas. Association for Computing Machinery, 2026.

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title = "Do People Appropriately Rely on AI-Advice? An Analytical Review of HCI Research on Human-AI Decision-Making",

abstract = "AI systems are increasingly being positioned to assist people in decision-making. However, recent empirical studies show critical concerns that people over-rely on AI advice without analytically engaging with it. While HCI research explores how people rely on AI advice, we argue that it largely overlooks an important aspect: replicating realistic decision-making scenarios. Human-AI interaction factors influence people's reliance on AI advice. To understand human-AI interaction factors and their interplay, we conducted an analytical review of recent studies in human-AI reliance literature. We analyzed the decision-making tasks in research and their validity in application-grounded contexts. Our findings show that user engagement is a precious commodity for relying on AI advice; however, it comes at a cost. We also discuss factors contributing to {"}appropriate reliance{"}, existing research gaps, and recommendations for intervention design for human-AI reliance. Our work contributes to the critical body of research on building appropriate reliance on AI advice.",

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author = "Muhammad Raees and Khan, \{Vassilis Javed\} and Ioanna Lykourentzou and Konstantinos Papangelis",

note = "Publisher Copyright: {\textcopyright} 2026 Copyright held by the owner/author(s).; 2026 CHI Conference on Human Factors in Computing Systems, CHI 2026 ; Conference date: 13-04-2026 Through 17-04-2026",

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doi = "10.1145/3772318.3791467",

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Raees, M, Khan, VJ, Lykourentzou, I & Papangelis, K 2026, Do People Appropriately Rely on AI-Advice? An Analytical Review of HCI Research on Human-AI Decision-Making. in N Oliver, DA Shamma, H Candello, P Cesar, P Lopes, A Bozzon, T Kosch, V Liao, X Ma, V Artizzu, F Draxler, G Lopez, AV Reinschluessel, X Tong & PO Toups Dugas (eds), CHI 2026 - Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems., 805, Association for Computing Machinery, 2026 CHI Conference on Human Factors in Computing Systems, CHI 2026, Barcelona, Spain, 13/04/26. https://doi.org/10.1145/3772318.3791467

Do People Appropriately Rely on AI-Advice? An Analytical Review of HCI Research on Human-AI Decision-Making. / Raees, Muhammad; Khan, Vassilis Javed; Lykourentzou, Ioanna et al.
CHI 2026 - Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. ed. / Nuria Oliver; David A. Shamma; Heloisa Candello; Pablo Cesar; Pedro Lopes; Alessandro Bozzon; Thomas Kosch; Vera Liao; Xiaojuan Ma; Valentino Artizzu; Fiona Draxler; Gustavo Lopez; Anke V. Reinschluessel; Xin Tong; Phoebe O. Toups Dugas. Association for Computing Machinery, 2026. 805.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Do People Appropriately Rely on AI-Advice? An Analytical Review of HCI Research on Human-AI Decision-Making

AU - Raees, Muhammad

AU - Khan, Vassilis Javed

AU - Lykourentzou, Ioanna

AU - Papangelis, Konstantinos

PY - 2026/4/13

Y1 - 2026/4/13

N2 - AI systems are increasingly being positioned to assist people in decision-making. However, recent empirical studies show critical concerns that people over-rely on AI advice without analytically engaging with it. While HCI research explores how people rely on AI advice, we argue that it largely overlooks an important aspect: replicating realistic decision-making scenarios. Human-AI interaction factors influence people's reliance on AI advice. To understand human-AI interaction factors and their interplay, we conducted an analytical review of recent studies in human-AI reliance literature. We analyzed the decision-making tasks in research and their validity in application-grounded contexts. Our findings show that user engagement is a precious commodity for relying on AI advice; however, it comes at a cost. We also discuss factors contributing to "appropriate reliance", existing research gaps, and recommendations for intervention design for human-AI reliance. Our work contributes to the critical body of research on building appropriate reliance on AI advice.

AB - AI systems are increasingly being positioned to assist people in decision-making. However, recent empirical studies show critical concerns that people over-rely on AI advice without analytically engaging with it. While HCI research explores how people rely on AI advice, we argue that it largely overlooks an important aspect: replicating realistic decision-making scenarios. Human-AI interaction factors influence people's reliance on AI advice. To understand human-AI interaction factors and their interplay, we conducted an analytical review of recent studies in human-AI reliance literature. We analyzed the decision-making tasks in research and their validity in application-grounded contexts. Our findings show that user engagement is a precious commodity for relying on AI advice; however, it comes at a cost. We also discuss factors contributing to "appropriate reliance", existing research gaps, and recommendations for intervention design for human-AI reliance. Our work contributes to the critical body of research on building appropriate reliance on AI advice.

KW - AI Reliance

KW - Appropriate Reliance

KW - Human-AI Decision Making

KW - Human-AI Reliance

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DO - 10.1145/3772318.3791467

M3 - Conference contribution

AN - SCOPUS:105038730784

SN - 979-8-4007-2278-3

BT - CHI 2026 - Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems

A2 - Oliver, Nuria

A2 - Shamma, David A.

A2 - Candello, Heloisa

A2 - Cesar, Pablo

A2 - Lopes, Pedro

A2 - Bozzon, Alessandro

A2 - Kosch, Thomas

A2 - Liao, Vera

A2 - Ma, Xiaojuan

A2 - Artizzu, Valentino

A2 - Draxler, Fiona

A2 - Lopez, Gustavo

A2 - Reinschluessel, Anke V.

A2 - Tong, Xin

A2 - Toups Dugas, Phoebe O.

PB - Association for Computing Machinery

T2 - 2026 CHI Conference on Human Factors in Computing Systems, CHI 2026

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ER -

Raees M, Khan VJ, Lykourentzou I, Papangelis K. Do People Appropriately Rely on AI-Advice? An Analytical Review of HCI Research on Human-AI Decision-Making. In Oliver N, Shamma DA, Candello H, Cesar P, Lopes P, Bozzon A, Kosch T, Liao V, Ma X, Artizzu V, Draxler F, Lopez G, Reinschluessel AV, Tong X, Toups Dugas PO, editors, CHI 2026 - Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery. 2026. 805 doi: 10.1145/3772318.3791467

Do People Appropriately Rely on AI-Advice? An Analytical Review of HCI Research on Human-AI Decision-Making

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Bibliographical note

Keywords

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