Factors influencing trust in algorithmic decision-making: an indirect scenario-based experiment

Algorithms are involved in decisions ranging from trivial to significant, but people often express distrust toward them. Research suggests that educational efforts to explain how algorithms work may help mitigate this distrust. In a study of 1,921 participants from 20 countries, we examined differences in algorithmic trust for low-stakes and high-stakes decisions. Our results suggest that statistical literacy is negatively associated with trust in algorithms for high-stakes situations, while it is positively associated with trust in low-stakes scenarios with high algorithm familiarity. However, explainability did not appear to influence trust in algorithms. We conclude that having statistical literacy enables individuals to critically evaluate the decisions made by algorithms, data and AI, and consider them alongside other factors before making significant life decisions. This ensures that individuals are not solely relying on algorithms that may not fully capture the complexity and nuances of human behavior and decision-making. Therefore, policymakers should consider promoting statistical/AI literacy to address some of the complexities associated with trust in algorithms. This work paves the way for further research, including the triangulation of data with direct observations of user interactions with algorithms or physiological measures to assess trust more accurately.

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Publication Details

Journal
Frontiers in Artificial Intelligence
Published
2025-02-04
DOI
https://doi.org/10.3389/frai.2024.1465605
Citations
14
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
33.12
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article

Factors influencing trust in algorithmic decision-making: an indirect scenario-based experiment

José Antonio Hinojosa, Yuki Yamada, Izuchukwu L. G. Ndukaihe, Natalia Trujillo et al.
14 citations
Frontiers in Artificial Intelligence
Ethics and Social Impacts of AI
33.12
article

Factors influencing trust in algorithmic decision-making: an indirect scenario-based experiment

José Antonio Hinojosa, Yuki Yamada, Izuchukwu L. G. Ndukaihe, Natalia Trujillo, Talal Rahwan, Omid Khatin‐Zadeh, Armina Janyan, Meiselina Irmayanti, Roberto Giorgini, Julian Posada, George Siemens, Kelsey E. Medeiros, Belona Sonna, Nwadiogo Chisom Arinze, Rebecca Marrone, Pedro R. Montoro, Małgorzata Korolkiewicz, Bidisha Som, Kiril Kostov, Josef Kundrát, Maria Sahakyan, Julián Tejada, Asil Ali Özdoğru, Chun-Chia Kung, Lenka Skanderová, Kris Ariyabuddhiphongs, Hassan Banaruee, Raquel Meister Ko. Freitag, Fernando Marmolejo‐Ramos, Marco Tullio Liuzza, V. Thanh Ngo, Aidos Bolatov, Srécko Joksimovíc, Yuki Mori, Giang T. Nguyen, Thomas K. Hunt, Stella Valencia, Abdul Muktadir, Florence Gabriel, Assylbek Meirmanov, Michelle Lacia, Fransiska Timoria Samosir, Wachirawit Rakchai
article en
14 citations

Abstract

Algorithms are involved in decisions ranging from trivial to significant, but people often express distrust toward them. Research suggests that educational efforts to explain how algorithms work may help mitigate this distrust. In a study of 1,921 participants from 20 countries, we examined differences in algorithmic trust for low-stakes and high-stakes decisions. Our results suggest that statistical literacy is negatively associated with trust in algorithms for high-stakes situations, while it is positively associated with trust in low-stakes scenarios with high algorithm familiarity. However, explainability did not appear to influence trust in algorithms. We conclude that having statistical literacy enables individuals to critically evaluate the decisions made by algorithms, data and AI, and consider them alongside other factors before making significant life decisions. This ensures that individuals are not solely relying on algorithms that may not fully capture the complexity and nuances of human behavior and decision-making. Therefore, policymakers should consider promoting statistical/AI literacy to address some of the complexities associated with trust in algorithms. This work paves the way for further research, including the triangulation of data with direct observations of user interactions with algorithms or physiological measures to assess trust more accurately.

Frontiers in Artificial IntelligenceVol. 7
New York University Abu Dhabi (AE), Universidad Complutense de Madrid (ES), University of Nebraska at Omaha (US), New Bulgarian University (BG), Indian Institute of Technology Guwahati (IN), Kyushu University (JP), University of Padua (IT), VSB - Technical University of Ostrava (CZ), University of Electronic Science and Technology of China (CN), Chulalongkorn University (TH), Flinders University (AU), University of South Australia (AU), Universidad Nacional de Educación a Distancia (ES), University of Ostrava (CZ), Universidade Federal de Sergipe (BR), University of Derby (GB), Universidad Nebrija (ES), Yale University (US), Universidad de Antioquia (CO), Magna Graecia University (IT), University of Education Weingarten (DE), African Institute for Mathematical Sciences (CM), Astana Medical University (KZ), Notre Dame University (PH), University of Bengkulu (ID), Hanoi University (VN), Marmara University (TR), National Cheng Kung University (TW)
Quality Education
Openalex Percentile: Top 0%
Ethics and Social Impacts of AI
33.12
DOIOpen Access PDFOpenAlex
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