Factors Influencing Trust in Algorithmic Decision-Making: An Indirect Scenario-Based Experiment
Fernando Marmolejo-Ramos, Rebecca Marrone, Malgorzata Korolkiewicz, Florence Gabriel, George Siemens, Srecko Joksimovic, Yuki Yamada, Yuki Mori, Talal Rahwan, Maria Sahakyan, Belona Sonna, Assylbek Meirmanov, Aidos Bolatov, Bidisha Som, Izuchukwu Ndukaihe, Nwadiogo C. Arinze, Josef Kundrát, Lenka Skanderová, Van-Giang Ngo, Giang Nguyen, Michelle Lacia, Chun-Chia Kung, Meiselina Irmayanti, Abdul Muktadir, Fransiska Timoria Samosir, Marco Tullio Liuzza, Roberto Giorgini, Omid Khatin-Zadeh, Hassan Banaruee, Asil Ali Özdoğru, Kris Ariyabuddhiphongs, Wachirawit Rakchai, Natalia Trujillo, Stella Maris Valencia, Armina Janyan, Kiril Kostov, Pedro R. Montoro, Jose Hinojosa, Kelsey Medeiros, Thomas E. Hunt, Julian Posada, Raquel Meister Ko Freitag, and Julian Tejada
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.
Fernando Marmolejo-Ramos, Rebecca Marrone, Malgorzata Korolkiewicz, Florence Gabriel, George Siemens, Srecko Joksimovic, Yuki Yamada, Yuki Mori, Talal Rahwan, Maria Sahakyan, Belona Sonna, Assylbek Meirmanov, Aidos Bolatov, Bidisha Som, Izuchukwu Ndukaihe, Nwadiogo C. Arinze, Josef Kundrát, Lenka Skanderová, Van-Giang Ngo, Giang Nguyen, Michelle Lacia, Chun-Chia Kung, Meiselina Irmayanti, Abdul Muktadir, Fransiska Timoria Samosir, Marco Tullio Liuzza, Roberto Giorgini, Omid Khatin-Zadeh, Hassan Banaruee, Asil Ali Özdoğru, Kris Ariyabuddhiphongs, Wachirawit Rakchai, Natalia Trujillo, Stella Maris Valencia, Armina Janyan, Kiril Kostov, Pedro R. Montoro, Jose Hinojosa, Kelsey Medeiros, Thomas E. Hunt, Julian Posada, Raquel Meister Ko Freitag, and Julian Tejada
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.Keywords: algorithms, data, AI, trust, statistical literacy, explainability
Citation: Marmolejo-Ramos, F., Marrone, R., Korolkiewicz, M., Gabriel, F., Siemens, G., Joksimovic, S., Yamada, Y., Mori, Y., Rahwan, T., Sahakyan, M., Sonna, B., Meirmanov, A., Bolatov, A., Som, B., Ndukaihe, I., Arinze, N. C., Kundrát, J., Skanderová, L., Ngo, V.-G., . . . Tejada, J. (2025). Factors influencing trust in algorithmic decision-making: An indirect scenario-based experiment. Frontiers in Artificial Intelligence, 7, 1465605. https://doi.org/10.3389/frai.2024.1465605
