Relevant bibliographies by topics / Algorithm aversion AI

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Algorithm aversion AI'

Author: Grafiati
Published: 29 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Algorithm aversion AI"

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Hou, Yoyo Tsung-Yu, and Malte F. Jung. "Who is the Expert? Reconciling Algorithm Aversion and Algorithm Appreciation in AI-Supported Decision Making." Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021): 1–25. http://dx.doi.org/10.1145/3479864.

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The increased use of algorithms to support decision making raises questions about whether people prefer algorithmic or human input when making decisions. Two streams of research on algorithm aversion and algorithm appreciation have yielded contradicting results. Our work attempts to reconcile these contradictory findings by focusing on the framings of humans and algorithms as a mechanism. In three decision making experiments, we created an algorithm appreciation result (Experiment 1) as well as an algorithm aversion result (Experiment 2) by manipulating only the description of the human agent
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Fine, Anna, Stephanie Le, and Monica K. Miller. "Content Analysis of Judges' Sentiments Toward Artificial Intelligence Risk Assessment Tools." Journal of Criminology, Criminal Justice, Law & Society 24, no. 2 (2023): 31–46. http://dx.doi.org/10.54555/ccjls.8169.84869.

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Artificial intelligence (AI) uses computer programming to make predictions (e.g., bail decisions) and has the potential to benefit the justice system (e.g., save time and reduce bias). This secondary data analysis assessed 381 judges’ responses to the question, “Do you feel that artificial intelligence (using computer programs and algorithms) holds promise to remove bias from bail and sentencing decisions?” The authors created apriori themes based on the literature, which included judges’ algorithm aversion and appreciation, locus of control, procedural justice, and legitimacy. Results suggest
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Fine, A., S. Le, and M. K. Miller. "Content Analysis of Judges’ Sentiments Toward Artificial Intelligence Risk Assessment Tools." Russian Journal of Economics and Law 18, no. 1 (2024): 246–63. http://dx.doi.org/10.21202/2782-2923.2024.1.246-263.

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Objective: to analyze the positions of judges on risk assessment tools using artificial intelligence.Methods: dialectical approach to cognition of social phenomena, allowing to analyze them in historical development and functioning in the context of the totality of objective and subjective factors, which predetermined the following research methods: formal-logical and sociological.Results: Artificial intelligence (AI) uses computer programming to make predictions (e.g., bail decisions) and has the potential to benefit the justice system (e.g., save time and reduce bias). This secondary data an
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Adekunle Adeyemi, Oghenemarho Karieren, Hassan Olugbile, Victory Ikechi Okwe, and Fawaz Haroun. "A review on algorithm aversion, appreciation, and investor return beliefs." International Journal of Science and Research Archive 16, no. 1 (2025): 126–33. https://doi.org/10.30574/ijsra.2025.16.1.1968.

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As artificial intelligence (AI) continues to transform financial decision-making, responses of investors toward algorithmic tools have varied from rejection to voluntary adoption. This review looks at two different behavioral outcomes: algorithm aversion, or resistance to machine-provided advice even when it has been validated, and algorithm appreciation, where investors prefer algorithmic advice under certain particular conditions. Drawing on behavioral finance, psychology, and decision theory research, the review examines how such beliefs influence investor return beliefs i.e., the subjectiv
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Fleiß, Jürgen, Elisabeth Bäck, and Stefan Thalmann. "Mitigating Algorithm Aversion in Recruiting: A Study on Explainable AI for Conversational Agents." ACM SIGMIS Database: the DATABASE for Advances in Information Systems 55, no. 1 (2024): 56–87. http://dx.doi.org/10.1145/3645057.3645062.

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The use of conversational agents (CAs) based on artificial intelligence (AI) is becoming more common in the field of recruiting. Organizations are now adopting AI-based CAs for applicant (pre-)selection, but negative news coverage, especially the black-box character of AI, has hindered adoption. So far, little is known about the contextual factors influencing users' perception of AI-based CAs in general and the effect of provided explanations by explainable AI (XAI) in particular. While research on algorithm aversion provides some initial explanations, information regarding the effects of diff
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Turel, Ofir, and Shivam Kalhan. "Prejudiced against the Machine? Implicit Associations and the Transience of Algorithm Aversion." MIS Quarterly 47, no. 4 (2023): 1369–94. http://dx.doi.org/10.25300/misq/2022/17961.

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Algorithm aversion is an important and persistent issue that prevents harvesting the benefits of advancements in artificial intelligence. The literature thus far has provided explanations that primarily focus on conscious reflective processes. Here, we supplement this view by taking an unconscious perspective that can be highly informative. Building on theories of implicit prejudice, in a preregistered study, we suggest that people develop an implicit bias (i.e., prejudice) against artificial intelligence (AI) systems, as a different and threatening “species,” the behavior of which is unknown.
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Campion, John R., Donal B. O'Connor, and Conor Lahiff. "Human-artificial intelligence interaction in gastrointestinal endoscopy." World Journal of Gastrointestinal Endoscopy 16, no. 3 (2024): 126–35. http://dx.doi.org/10.4253/wjge.v16.i3.126.

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The number and variety of applications of artificial intelligence (AI) in gastrointestinal (GI) endoscopy is growing rapidly. New technologies based on machine learning (ML) and convolutional neural networks (CNNs) are at various stages of development and deployment to assist patients and endoscopists in preparing for endoscopic procedures, in detection, diagnosis and classification of pathology during endoscopy and in confirmation of key performance indicators. Platforms based on ML and CNNs require regulatory approval as medical devices. Interactions between humans and the technologies we us
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Longoni, Chiara, Andrea Bonezzi, and Carey K. Morewedge. "Resistance to medical artificial intelligence is an attribute in a compensatory decision process: response to Pezzo and Beckstead (2020)." Judgment and Decision Making 15, no. 3 (2020): 446–48. http://dx.doi.org/10.1017/s1930297500007233.

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AbstractIn Longoni et al. (2019), we examine how algorithm aversion influences utilization of healthcare delivered by human and artificial intelligence providers. Pezzo and Beckstead’s (2020) commentary asks whether resistance to medical AI takes the form of a noncompensatory decision strategy, in which a single attribute determines provider choice, or whether resistance to medical AI is one of several attributes considered in a compensatory decision strategy. We clarify that our paper both claims and finds that, all else equal, resistance to medical AI is one of several attributes (e.g., cost
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Downen, Tom, Sarah Kim, and Lorraine Lee. "Algorithm aversion, emotions, and investor reaction: Does disclosing the use of AI influence investment decisions?" International Journal of Accounting Information Systems 52 (March 2024): 100664. http://dx.doi.org/10.1016/j.accinf.2023.100664.

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Pezzo, Mark V., and Jason W. Beckstead. "Algorithm aversion is too often presented as though it were non-compensatory: A reply to Longoni et al. (2020)." Judgment and Decision Making 15, no. 3 (2020): 449–51. http://dx.doi.org/10.1017/s1930297500007245.

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AbstractWe clarify two points made in our commentary (Pezzo & Beckstead, 2020, this issue) on a recent paper by Longoni, Bonezzi, and Morewedge (2019). In both Experiments 1 and 4 from their paper, it is not possible to determine whether accuracy can compensate for algorithm aversion. Experiments 3A-C, however, do show a strong effect of accuracy such that AI that is superior to a human provider is embraced by patients. Many papers, including Longoni et al. tend to minimize the role of this compensatory process, apparently because it seems obvious to the authors (Longoni, Bonezzi, Morewedg
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Dissertations / Theses on the topic "Algorithm aversion AI"

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Strömstedt, Björn. "Algorithm aversion in scenarios with acquisition and forfeiture framing." Thesis, Linköpings universitet, Filosofiska fakulteten, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177160.

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Humankind is becoming increasingly dependent on algorithms in their everyday life. Algorithmic decision support has existed since the entrance of computers but are becoming more sophisticated with elements of Articial Intelligence (AI). Though many decision support systems outperform humans in many areas, e.g. in forecasting task, the willingness to trust and use algorithmic decision support is lower than in a corresponding human. Many factors have been investigated to why this algorithm aversion exists but there is a gap in research about the eects of scenario characteristics. Results provide
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Jönsson, Josef. "AI acceptance and attitudes : People’s perception of healthcare and commercial AI applications." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176507.

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The relevance of AI is ever increasing. The goal of the wide implementation is usually either to boost task efficiency or for public comfort. To fuel this progression, more personal data is being used and Artificial intelligence inhabits the role of the human expert, in many different applications. This study investigated the attitudes and rates of acceptance to said AI applications and if they differed in relation to each other. Additionally, this study explored if general positive and negative attitude towards AI influence AI acceptance. Applications studied came from two different domains,
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Årnfelt, Theodor. "Risk-benefit perception of AI use : Public perception of AI in healthcare and commercial domains." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177750.

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AI applications are today implemented in a wide range of settings with the goal of achieving greater efficiency. However, these implementations can not be guaranteed to be free of risks. This study investigated how people perceive these risks and benefits, and whether there were any notable differences to be found between the domains in which they appear, in this case e-commerce/marketing and healthcare. Additionally, the relationship between the perceptions and individual positive and negative attitudes towards AI were examined by utilizing an affect heuristic framework. The findings showed t
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Book chapters on the topic "Algorithm aversion AI"

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Magni, Federico, Heather Yang, and Yaping Gong. "The Facets and Consequences of Uncertainty in Human–AI Interaction." In The Oxford Handbook of Uncertainty Management in Work Organizations. Oxford University Press, 2024. http://dx.doi.org/10.1093/oxfordhb/9780197501061.013.22.

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Abstract Artificial intelligence (AI) is increasingly used to assist or substitute humans in organizational processes. The transfer of decision-making power and task performance from humans to artificial agents increases uncertainty—defined as a state of not knowing stemming from incomplete and/or ambiguous information—for workers in several ways. Within the broader context of environmental and societal uncertainty, the authors focus specifically on uncertainty at the task level and develop a framework about uncertainty in human–AI interaction, showing that people experience three main facets
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Sawon, Md Mehedi Hasan, Farhana Yeasmin Lina, and Md Akram Hossain. "AI in the Tourism and Hospitality Industry in Bangladesh." In Advances in Hospitality, Tourism, and the Services Industry. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-6755-1.ch005.

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Artificial intelligence (AI) is reshaping industries globally, and the tourism and hospitality sector in Bangladesh presents a landscape ripe for transformation. This chapter explores AI adoption in Bangladesh's tourism and hospitality industry, analyzing data from academic literature, government, and industry reports, identifying utilization of artificial intelligence platforms, associated challenges, as well as ways forward. It examines the impacts of adopting AI-powered chatbots, virtual assistants, personalized recommendation engines, and advanced price optimization algorithms in the touri
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Conference papers on the topic "Algorithm aversion AI"

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Takagi, Hisashi, Yang Li, Masashi Komori, and Kazunori Terada. "Measuring Algorithm Aversion and Betrayal Aversion to Humans and AI using Trust Games." In 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN). IEEE, 2024. http://dx.doi.org/10.1109/ro-man60168.2024.10731215.

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Heid, Stefan, Jaroslaw Kornowicz, Jonas Hanselle, Eyke Hüllermeier, and Kirsten Thommes. "Human-AI Co-Construction of Interpretable Predictive Models: The Case of Scoring Systems." In 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024. KIT Scientific Publishing, 2024. https://doi.org/10.58895/ksp//1000174544-15.

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This study explores the co-construction of probabilistic scoring systems. Using a self-developed web-based tool, called PSLVIS, participants were able to create their own decision-support models through an interactive interface. Seven academic advising experts participated, assessing the probability of student success both with and without the assistance of a Probabilistic Scoring List (PSL). The results indicate that while the co-constructed models slightly improved the experts’ accuracy, they also increased decision time. Experts interacted with PSLVIS and PSL in diverse ways, displaying dif
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Prossinger, Hermann, Jakub Binter, Kamila Machová, Daniel Říha, and Silvia Boschetti. "Machine Learning Detects Pairwise Associations between SOI and BIS/BAS Subscales, making Correlation Analyses Obsolete." In Human Interaction and Emerging Technologies (IHIET-AI 2022) Artificial Intelligence and Future Applications. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe100902.

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We use AI techniques to statistically rigorously analyze combinations of query responses of two personality-related questionnaires. One questionnaire probes aspects of a participant’s character (SOI) and the other avoidance of aversive outcomes together with approaches to goal orientated outcomes (BIS/BAS). We use one-hot encoding, dimension reduction with a neural network (a seven-layer auto-encoder) and two clustering algorithms to detect associations between the twelve combinations of SOI and BIS/BAS groups. We discover that for most combinations more than one association exists. Traditiona
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