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Journal articles on the topic 'Algorithm aversion AI'

Author: Grafiati

Published: 29 June 2021

Last updated: 29 July 2025

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1

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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Malin, Christine, Cordula Kupfer, Jürgen Fleiß, Bettina Kubicek, and Stefan Thalmann. "In the AI of the Beholder—A Qualitative Study of HR Professionals’ Beliefs about AI-Based Chatbots and Decision Support in Candidate Pre-Selection." Administrative Sciences 13, no. 11 (2023): 231. http://dx.doi.org/10.3390/admsci13110231.

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Despite the high potential of artificial intelligence (AI), its actual adoption in recruiting is low. Explanations for this discrepancy are scarce. Hence, this paper presents an exploratory interview study investigating HR professionals’ beliefs about AI to examine their impact on use cases and barriers and to identify the reasons that lead to the non-adoption of AI in recruiting. Semi-structured interviews were conducted with 25 HR professionals from 21 companies. The results revealed that HR professionals’ beliefs about AI could be categorised along two dimensions: (1) the scope of AI and (2

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Kim, Hyo Young, and Young Soo Park. "Trust Dynamics in Financial Decision Making: Behavioral Responses to AI and Human Expert Advice Following Structural Breaks." Behavioral Sciences 14, no. 10 (2024): 964. http://dx.doi.org/10.3390/bs14100964.

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This study explores the trust dynamics in financial forecasting by comparing how individuals perceive the credibility of AI and human experts during significant structural market changes. We specifically examine the impact of two types of structural breaks on trust: Additive Outliers, which represent a single yet significant anomaly, and Level Shifts, which indicate a sustained change in data patterns. Grounded in theoretical frameworks such as attribution theory, algorithm aversion, and the Technology Acceptance Model (TAM), this research investigates psychological responses to AI and human a

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Gupta, Parth. "AI Driven Decision Support Systems for Business Operations." International Journal of Transformations in Business Management 14, no. 1 (2024): 94–100. https://doi.org/10.37648/ijtbm.v14i01.012.

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In the era of digital transformation, businesses are increasingly relying on intelligent systems to enhance operational efficiency and strategic decision-making. Artificial Intelligence-driven Decision Support Systems (AI-DSS) have emerged as a pivotal innovation, offering advanced capabilities such as predictive analytics, real-time optimization, and adaptive learning. This paper presents a comprehensive study on the development, implementation, and impact of AI-DSS across various business functions. It explores the integration of machine learning (ML), deep learning (DL), natural language pr

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Pe, Samuele, Lorenzo Famiglini, Enrico Gallazzi, et al. "Alternative Strategies to Generate Class Activation Maps Supporting AI-based Advice in Vertebral Fracture Detection in X-ray Images." Methods of Information in Medicine 63, no. 03/04 (2024): 122–36. https://doi.org/10.1055/a-2562-2163.

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AbstractBalancing artificial intelligence (AI) support with appropriate human oversight is challenging, with associated risks such as algorithm aversion and technology dominance. Research areas like eXplainable AI (XAI) and Frictional AI aim to address these challenges. Studies have shown that presenting XAI explanations as “juxtaposed evidence” supporting contrasting classifications, rather than just providing predictions, can be beneficial.This study aimed to design and compare multiple pipelines for generating juxtaposed evidence in the form of class activation maps (CAMs) that highlight ar

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Tahvildari, Mahan. "Integrating generative AI in Robo-Advisory: A systematic review of opportunities, challenges, and strategic solutions." Multidisciplinary Reviews 8, no. 12 (2025): 2025379. https://doi.org/10.31893/multirev.2025379.

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The integration of generative AI into financial advisory services marks a significant advancement in portfolio optimization, risk assessment, and decision support and recent developments in large language models (LLMs), such as ChatGPT, have demonstrated the ability to process both structured financial data and unstructured market sentiment, enhancing the accuracy and adaptability of investment recommendations. However, the application of generative AI in robo-advisory systems presents ethical, regulatory, and psychological challenges and this study conducts a systematic literature review to e

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Staff, Marta, Jo Butts, and Chat GPT. "From Human Hands to Algorithmic Minds: WHATIF Machines Become Collaborators in Artistic Creation?" Open Review 10, no. 1 (2025): 42–53. https://doi.org/10.47967/tor10fn9s.

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The nature of consciousness remains a central debate in the philosophy of mind and cognitive science, with no unanimous definition or understanding of its essence. At its core, consciousness involves the first-person experience of sensory perception, thoughts, feelings, and other mental states- the subjective awareness of “what it is like” to have a particular experience. Conceptualisation, the ability to form mental representations of abstract ideas, plays a pivotal role in translating creative visions into tangible works. Artist's assistants and ghost writers are established practices showin

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Guo, Weiqi, and Yang Chen. "Investigating Whether AI Will Replace Human Physicians and Understanding the Interplay of the Source of Consultation, Health-Related Stigma, and Explanations of Diagnoses on Patients’ Evaluations of Medical Consultations: Randomized Factorial Experiment." Journal of Medical Internet Research 27 (March 5, 2025): e66760. https://doi.org/10.2196/66760.

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Background The increasing use of artificial intelligence (AI) in medical diagnosis and consultation promises benefits such as greater accuracy and efficiency. However, there is little evidence to systematically test whether the ideal technological promises translate into an improved evaluation of the medical consultation from the patient’s perspective. This perspective is significant because AI as a technological solution does not necessarily improve patient confidence in diagnosis and adherence to treatment at the functional level, create meaningful interactions between the medical agent and

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Moeed, Syed Abdul, Bellam Surendra Babu, M. Sreevani, B. V. Devendra Rao, R. Raja Kumar, and Gouse Baig Mohammed. "An Intelligent Crow Search Optimization and Bi-GRU for Forest Fire Detection System Using Internet of Things." Nature Environment and Pollution Technology 23, no. 4 (2024): 2355–70. https://doi.org/10.46488/nept.2024.v23i04.039.

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Natural ecosystems have been facing a major threat due to deforestation and forest fires for the past decade. These environmental challenges have led to significant biodiversity loss, disruption of natural habitats, and adverse effects on climate change. The integration of Artificial Intelligence (AI) and Optimization techniques has made a revolutionary impact in disaster management, offering new avenues for early detection and prevention strategies. Therefore, to prevent the outbreak of a forest fire, an efficient forest fire diagnosis and aversion system is needed. To address this problem, a

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Revillod, Guillaume. "Why Do Swiss HR Departments Dislike Algorithms in Their Recruitment Process? An Empirical Analysis." Administrative Sciences 14, no. 10 (2024): 253. http://dx.doi.org/10.3390/admsci14100253.

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This study investigates the factors influencing the aversion of Swiss HRM departments to algorithmic decision-making in the hiring process. Based on a survey provided to 324 private and public HR professionals, it explores how privacy concerns, general attitude toward AI, perceived threat, personal development concerns, and personal well-being concerns, as well as control variables such as gender, age, time with organization, and hierarchical position, influence their algorithmic aversion. Its aim is to understand the algorithmic aversion of HR employees in the private and public sectors. The

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Ronald Edison Carpio Chiriboga. "Integration of Artificial Intelligence and Behavioral Economics: Optimizing Consumer Processes and Decisions in Complex Environments." Journal of Information Systems Engineering and Management 10, no. 39s (2025): 620–25. https://doi.org/10.52783/jisem.v10i39s.7257.

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This study presents a comprehensive approach that combines artificial intelligence (AI) and behavioral economics to optimize industrial processes and better understand consumer decisions in complex and uncertain environments, such as inflationary contexts. Through a quantitative methodology in two phases, data from 1,200 consumers were analyzed and neural network models were implemented in 50 manufacturing companies in Latin America. In the first phase, cognitive biases such as loss aversion, anchoring effect and availability were evaluated, identifying their significant influence on irrationa

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Le, Huixiao, Yuan Shen, Zijian Li, et al. "Breaking human dominance: Investigating learners' preferences for learning feedback from generative AI and human tutors." British Journal of Educational Technology, July 4, 2025. https://doi.org/10.1111/bjet.13614.

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AbstractUnderstanding learners' preferences in educational settings is crucial for optimizing learning outcomes and experience. As artificial intelligence (AI) becomes increasingly integrated into educational contexts, it is crucial to understand learners' preferences between AI and human tutors to support their learning. While AI demonstrates growing potential in education, the phenomenon of algorithm aversion, which is a tendency to favour human decision making over algorithmic solutions, requires further investigation. To explore this issue, an experiment involving 114 university students w

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Silber, Dietrich, Arvid Hoffmann, and Alex Belli. "Embracing AI advisors for making (complex) financial decisions: an experimental investigation of the role of a maximizing decision-making style." International Journal of Bank Marketing, March 11, 2025. https://doi.org/10.1108/ijbm-10-2024-0647.

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PurposeThis study investigates the impact of experimentally priming a maximizing decision-making style on individuals’ likelihood of using artificial intelligence (AI) advisors for making complex financial decisions, such as building an investment portfolio for their retirement. It examines whether individuals with stronger maximizing tendencies are more likely to perceive algorithms as effective, thereby reducing their algorithm aversion, and ultimately increasing the likelihood of using AI advisors in their financial decision-making.Design/methodology/approachA qualitative pre-study amongst

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Jenkin, Tracy, Stephanie Kelley, Anton Ovchinnikov, and Cecilia Ying. "Explanation seeking and anomalous recommendation adherence in human‐to‐human versus human‐to‐artificial intelligence interactions." Decision Sciences, November 21, 2024. http://dx.doi.org/10.1111/deci.12658.

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AbstractThe use of artificial intelligence (AI) in operational decision‐making is growing, but individuals can display algorithm aversion, preventing adherence to AI system recommendations—even when the system outperforms human decision‐makers. Understanding why such algorithm aversion occurs and how to reduce it is important to ensure AI is fully leveraged. While the ability to seek an explanation from an AI may be a promising approach to mitigate this aversion, there is conflicting evidence on their benefits. Based on several behavioral theories, including Bayesian choice, loss aversion, and

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Koo, Jieun. "AI is not careful: approach to the stock market and preference for AI advisor." International Journal of Bank Marketing, July 15, 2024. http://dx.doi.org/10.1108/ijbm-10-2023-0568.

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PurposeFinancial institutions actively seek to leverage the capabilities of artificial intelligence (AI) across diverse operations in the field. Especially, the adoption of AI advisors has a significant impact on trading and investing in the stock market. The purpose of this paper is to test whether AI advisors are less preferred compared to human advisors for investing and whether this algorithm aversion diminishes for trading.Design/methodology/approachThe four hypotheses regarding the direct and indirect relationships between variables are tested in five experiments that collect data from P

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Liu, Meng, Xiaocheng Tang, Siyuan Xia, Shuo Zhang, Yuting Zhu, and Qianying Meng. "Algorithm Aversion: Evidence from Ridesharing Drivers." Management Science, October 3, 2023. http://dx.doi.org/10.1287/mnsc.2022.02475.

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The low rate of adoption by human users often hinders AI algorithms from achieving their intended efficiency gains. This is particularly true for algorithms that prioritize system-wide objectives because they can create misalignment of incentives and cause confusion among potential users. We provide one of the first large-scale field studies on algorithm aversion by leveraging an algorithmic recommendation rollout on a large ridesharing platform. We identify contextual experience and herding as two important factors that explain ridesharing drivers’ aversion to an algorithm that is designed to

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Commerford, Benjamin P., Aasmund Eilifsen, Richard C. Hatfield, Kathryn M. Holmstrom, and Finn Kinserdal. "Control issues: How providing input affects auditors' reliance on artificial intelligence." Contemporary Accounting Research, September 12, 2024. http://dx.doi.org/10.1111/1911-3846.12974.

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AbstractIn this study, we examine auditors' reliance on artificial intelligence (AI) systems that are designed to provide evidence around complex estimates. In an experiment with highly experienced auditors, we find that auditors are more hesitant to rely on evidence from AI‐based systems compared to human specialists, consistent with algorithm aversion. Importantly, we also find that a small amount of control (i.e., providing input to specialists) can mitigate this aversion, though this effect depends on auditors' personal locus of control (LOC). Providing input increases reliance on evidence

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Talebi, Arash, Sourjo Mukherjee, Nazia Gera, Kulwinder Kaur, and Gopal Das. "Unveiling coping mechanisms in marketplace discrimination: The allure of artificial intelligence recommendations." Journal of Product Innovation Management, January 6, 2025. https://doi.org/10.1111/jpim.12774.

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AbstractDespite artificial intelligence's (AI) increased efficiency and accuracy in many contexts, algorithm aversion, that is, people's biased preference for human recommendations over those of algorithms, is a well‐documented phenomenon. In this research, we show a reversal of the algorithm aversion phenomenon, referred to as algorithm appreciation, in the prevalent context of marketplace discrimination. Specifically, the current research documents people's increased propensity to rely on AI‐based recommendations over those proposed by human counterparts in the aftermath of marketplace discr

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Buder, Jürgen, Fritz Becker, Janika Bareiß, and Markus Huff. "Beyond Mere Algorithm Aversion: Are Judgments About Computer Agents More Variable?" Communication Research, December 11, 2024. https://doi.org/10.1177/00936502241303588.

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Several studies have reported algorithm aversion, reflected in harsher judgments about computers that commit errors, compared to humans who commit the same errors. Two online studies ( N = 67, N = 252) tested whether similar effects can be obtained with a referential communication task. Participants were tasked with identifying Japanese kanji characters based on written descriptions allegedly coming from a human or an AI source. Crucially, descriptions were either flawed (ambiguous) or not. Both concurrent measures during experimental trials and pre-post questionnaire data about the source wer

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Xu, Min, and Yiwen Wang. "Explainability increases trust resilience in intelligent agents." British Journal of Psychology, October 21, 2024. http://dx.doi.org/10.1111/bjop.12740.

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AbstractEven though artificial intelligence (AI)‐based systems typically outperform human decision‐makers, they are not immune to errors, leading users to lose trust in them and be less likely to use them again—a phenomenon known as algorithm aversion. The purpose of the present research was to investigate whether explainable AI (XAI) could function as a viable strategy to counter algorithm aversion. We conducted two experiments to examine how XAI influences users' willingness to continue using AI‐based systems when these systems exhibit errors. The results showed that, following the observati

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Berger, Benedikt, Martin Adam, Alexander Rühr, and Alexander Benlian. "Watch Me Improve—Algorithm Aversion and Demonstrating the Ability to Learn." Business & Information Systems Engineering, December 4, 2020. http://dx.doi.org/10.1007/s12599-020-00678-5.

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AbstractOwing to advancements in artificial intelligence (AI) and specifically in machine learning, information technology (IT) systems can support humans in an increasing number of tasks. Yet, previous research indicates that people often prefer human support to support by an IT system, even if the latter provides superior performance – a phenomenon called algorithm aversion. A possible cause of algorithm aversion put forward in literature is that users lose trust in IT systems they become familiar with and perceive to err, for example, making forecasts that turn out to deviate from the actua

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Horowitz, Michael C., and Lauren Kahn. "Bending the Automation Bias Curve: A Study of Human and AI-Based Decision Making in National Security Contexts." International Studies Quarterly 68, no. 2 (2024). http://dx.doi.org/10.1093/isq/sqae020.

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Abstract Uses of artificial intelligence (AI) are growing around the world. What will influence AI adoption in the international security realm? Research on automation bias suggests that humans can often be overconfident in AI, whereas research on algorithm aversion shows that, as the stakes of a decision rise, humans become more cautious about trusting algorithms. We theorize about the relationship between background knowledge about AI, trust in AI, and how these interact with other factors to influence the probability of automation bias in the international security context. We test these in

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Zhang, Yunhao, and Renee Gosline. "Understanding Algorithm Aversion: When Do People Abandon AI After Seeing It Err?" SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4299576.

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Haupt, Martin, Jan Freidank, and Alexander Haas. "Consumer responses to human-AI collaboration at organizational frontlines: strategies to escape algorithm aversion in content creation." Review of Managerial Science, April 4, 2024. http://dx.doi.org/10.1007/s11846-024-00748-y.

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AbstractAlthough Artificial Intelligence can offer significant business benefits, many consumers have negative perceptions of AI, leading to negative reactions when companies act ethically and disclose its use. Based on the pervasive example of content creation (e.g., via tools like ChatGPT), this research examines the potential for human-AI collaboration to preserve consumers' message credibility judgments and attitudes towards the company. The study compares two distinct forms of human-AI collaboration, namely AI-supported human authorship and human-controlled AI authorship, with traditional

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Kennedy, Ryan, Lydia Tiede, Amanda Austin, and Kenzy Ismael. "Law Enforcement and Legal Professionals’ Trust in Algorithms." Journal of Law & Empirical Analysis, April 3, 2025. https://doi.org/10.1177/2755323x251325594.

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AI algorithms are increasingly influencing decision-making in criminal justice, including tasks such as predicting recidivism and identifying suspects by their facial features. The increasing reliance on machine-assisted legal decision-making impacts the rights of criminal defendants, the work of law enforcement agents, the legal strategies taken by attorneys, the decisions made by judges, and the public’s trust in courts. As such, it is crucial to understand how the use of AI is perceived by the professionals who interact with algorithms. The analysis explores the connection between law enfor

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Zhuang, Mike, Eliane Deschrijver, Richard Ramsey, and Ofir Turel. "Comparing discriminatory behavior against AI and humans." Scientific Reports 15, no. 1 (2025). https://doi.org/10.1038/s41598-025-94631-9.

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Abstract Although discrimination is typically believed to occur from well-defined categories like ethnicity, disability, and sex, studies have found that discrimination persists in minimal conditions lacking such categories. Participants have been found to preferentially allocate resources based on seemingly arbitrary shared characteristics such as dot estimation choices. Here, we use a preregistered experiment (n = 500) to investigate whether humans discriminate in a similar manner when interacting with artificial intelligence (AI) agents that ostensibly made dot estimations. We hypothesized

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Brand, Joshua L. M. "Put Yourself in My Shoes: Revisiting the Moral Value of Algorithm Aversion Through Reciprocity and Vulnerability." Philosophy & Technology 38, no. 3 (2025). https://doi.org/10.1007/s13347-025-00911-7.

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Abstract This paper begins by exploring the phenomenon known as algorithm aversion, where users distrust AI and prefer human advice or decision-making even when they are aware of the algorithm’s superior performance. Current literature generally frames it as a misguided bias that harms decision accuracy and speed, likening it to a form of neo-Luddism. This view, however, overlooks the fact that the two groups (supporters and sceptics of algorithmic decisions) are speaking different moral languages: the supporters are outcome-orientated, arguing for accuracy and performance, while the sceptics

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Longoni, Chiara, Luca Cian, and Ellie J. Kyung. "EXPRESS: AI in the Government: Responses to Failures." Journal of Marketing Research, June 15, 2022, 002224372211101. http://dx.doi.org/10.1177/00222437221110139.

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Artificial Intelligence (AI) is pervading the government and transforming how public services are provided to consumers—from allocation of government benefits to enforcement of the law, monitoring of risks, and provision of services. Despite technological improvements, AI systems are fallible and may err. How do consumers respond when learning of AI’s failures? In thirteen preregistered studies ( N = 3,724) across policy areas, we show that algorithmic failures are generalized more broadly than human failures. We term this effect algorithmic transference, as it is an inferential process that g

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Zhang, Yunhao, and Renée Gosline. "Human favoritism, not AI aversion: People’s perceptions (and bias) toward generative AI, human experts, and human–GAI collaboration in persuasive content generation." Judgment and Decision Making 18 (2023). http://dx.doi.org/10.1017/jdm.2023.37.

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Abstract With the wide availability of large language models and generative AI, there are four primary paradigms for human–AI collaboration: human-only, AI-only (ChatGPT-4), augmented human (where a human makes the final decision with AI output as a reference), or augmented AI (where the AI makes the final decision with human output as a reference). In partnership with one of the world’s leading consulting firms, we enlisted professional content creators and ChatGPT-4 to create advertising content for products and persuasive content for campaigns following the aforementioned paradigms. First,

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SimanTov-Nachlieli, Ilanit. "More to Lose: The Adverse Effect of High Performance Ranking on Employees’ Preimplementation Attitudes Toward the Integration of Powerful AI Aids." Organization Science, October 18, 2024. http://dx.doi.org/10.1287/orsc.2023.17515.

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Despite the growing availability of algorithm-augmented work, algorithm aversion is prevalent among employees, hindering successful implementations of powerful artificial intelligence (AI) aids. Applying a social comparison perspective, this article examines the adverse effect of employees’ high performance ranking on their preimplementation attitudes toward the integration of powerful AI aids within their area of advantage. Five studies, using a weight estimation simulation (Studies 1–3), recall of actual job tasks (Study 4), and a workplace scenario (Study 5), provided consistent causal evid

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Hutson, James, and Daniel Plate. "The Algorithm of Fear: Unpacking Prejudice Against AI and the Mistrust of Technology." Journal of Innovation and Technology 2024, no. 1 (2024). https://doi.org/10.61453/joit.v2024no38.

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The mistrust of AI seen in the media, industry and education reflects deep-seated cultural anxieties, often comparable to societal prejudices like racism and sexism. Throughout history, literature and media have portrayed machines as antagonists, amplifying fears of technological obsolescence and identity loss. Despite the recent remarkable advancements in AI—particularly in creative and decision-making capacities—human resistance to its adoption persists, rooted in a combination of technophobia, algorithm aversion, and cultural narratives of dystopia. This review investigates the origins of t

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Gong, Yongzhi, Xiaofei Tang, and Haoyu Peng. "The effect of subjective understanding on patients’ trust in AI pharmacy intravenous admixture services." Frontiers in Psychology 15 (September 5, 2024). http://dx.doi.org/10.3389/fpsyg.2024.1437915.

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IntroductionMedical services are getting automated and intelligent. An emerging medical service is the AI pharmacy intravenous admixture service (PIVAS) that prepares infusions through robots. However, patients may distrust these robots. Therefore, this study aims to investigate the psychological mechanism of patients’ trust in AI PIVAS.MethodsWe conducted one field study and four experimental studies to test our hypotheses. Study 1 and 2 investigated patients’ trust of AI PIVAS. Study 3 and 4 examined the effect of subjective understanding on trust in AI PIVAS. Study 5 examined the moderating

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Proksch, Sebastian, Julia Schühle, Elisabeth Streeb, Finn Weymann, Teresa Luther, and Joachim Kimmerle. "The impact of text topic and assumed human vs. AI authorship on competence and quality assessment." Frontiers in Artificial Intelligence 7 (May 31, 2024). http://dx.doi.org/10.3389/frai.2024.1412710.

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BackgroundWhile Large Language Models (LLMs) are considered positively with respect to technological progress and abilities, people are rather opposed to machines making moral decisions. But the circumstances under which algorithm aversion or algorithm appreciation are more likely to occur with respect to LLMs have not yet been sufficiently investigated. Therefore, the aim of this study was to investigate how texts with moral or technological topics, allegedly written either by a human author or by ChatGPT, are perceived.MethodsIn a randomized controlled experiment, n = 164 participants read s

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Gill, Andrej, Robert M. Gillenkirch, Julia Ortner, and Louis Velthuis. "Dynamics of Reliance on Algorithmic Advice." Journal of Behavioral Decision Making 37, no. 4 (2024). http://dx.doi.org/10.1002/bdm.2414.

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ABSTRACTThis study examines the dynamics of human reliance on algorithmic advice in a situation with strategic interaction. Participants played the strategic game of Rock–Paper–Scissors (RPS) under various conditions, receiving algorithmic decision support while facing human or algorithmic opponents. Results indicate that participants often underutilize algorithmic recommendations, particularly after early errors, but increasingly rely on the algorithm following successful early predictions. This behavior demonstrates a sensitivity to decision outcomes, with asymmetry: rejecting advice consist

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Magni, Federico, Rachel Schlund, Kurt Gray, et al. "Human Workers at the Center: Algorithm Aversion and Appreciation as Reactions to AI in Organizations." Academy of Management Proceedings 2023, no. 1 (2023). http://dx.doi.org/10.5465/amproc.2023.12940symposium.

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Markus, André, Maximilian Baumann, Jan Pfister, Andreas Hotho, Astrid Carolus, and Carolin Wienrich. "The impact of algorithm awareness training on competent interaction with intelligent voice assistants." Discover Education 4, no. 1 (2025). https://doi.org/10.1007/s44217-025-00522-6.

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Abstract Intelligent Voice Assistants (IVAs) have become integral to many users' daily lives, using advanced algorithms to automate various tasks. Nevertheless, many users do not understand the underlying algorithms and how they work, posing potential risks to the competent and self-determined use of IVAs. This work develops three online training modules to promote algorithm awareness, providing (1) basic knowledge of algorithms, (2) risks posed by algorithms in IVAs, and (3) scientific evidence on algorithm aversion. A total of 110 participants were studied to analyze the training effects on

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Magni, Federico, Jiyoung Park, and Melody Manchi Chao. "Humans as Creativity Gatekeepers: Are We Biased Against AI Creativity?" Journal of Business and Psychology, September 14, 2023. http://dx.doi.org/10.1007/s10869-023-09910-x.

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AbstractWith artificial intelligence (AI) increasingly involved in the creation of organizational and commercial artifacts, human evaluators’ role as creativity gatekeepers of AI-produced artifacts will become critical for innovation processes. However, when humans evaluate creativity, their judgment is clouded by biases triggered by the characteristics of the creator. Drawing from folk psychology and algorithm aversion research, we examine whether the identity of the producer of a given artifact as artificial intelligence (AI) or human is a source of bias affecting people’s creativity evaluat

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Keppeler, Florian. "No Thanks, Dear AI! Understanding the Effects of Disclosure and Deployment of Artificial Intelligence in Public Sector Recruitment." Journal of Public Administration Research and Theory, May 20, 2023. http://dx.doi.org/10.1093/jopart/muad009.

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Abstract Applications based on artificial intelligence (AI) play an increasing role in the public sector and invoke political discussions. Research gaps exist regarding the disclosure effects—reactions to disclosure of the use of AI applications—and the deployment effect—efficiency gains in data savvy tasks. This study analyzes disclosure effects and explores the deployment of an AI application in a pre-registered field experiment (n=2,000) co-designed with a public organization in the context of employer-driven recruitment. The linear regression results show that disclosing the use of the AI

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Roser, David, Michael Meinikheim, Anna Muzalyova, et al. "Artificial Intelligence‐assisted Endoscopy and Examiner Confidence: A Study on Human–Artificial Intelligence Interaction in Barrett's Esophagus (With Video)." DEN Open 6, no. 1 (2025). https://doi.org/10.1002/deo2.70150.

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ABSTRACTObjectiveDespite high stand‐alone performance, studies demonstrate that artificial intelligence (AI)‐supported endoscopic diagnostics often fall short in clinical applications due to human‐AI interaction factors. This video‐based trial on Barrett's esophagus aimed to investigate how examiner behavior, their levels of confidence, and system usability influence the diagnostic outcomes of AI‐assisted endoscopy.MethodsThe present analysis employed data from a multicenter randomized controlled tandem video trial involving 22 endoscopists with varying degrees of expertise. Participants were

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Seymour, Mike, Lingyao (Ivy) Yuan, Kai Riemer, and Alan R. Dennis. "Less Artificial, More Intelligent: Understanding Affinity, Trustworthiness, and Preference for Digital Humans." Information Systems Research, September 2, 2024. http://dx.doi.org/10.1287/isre.2022.0203.

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Practice- and policy-oriented abstract: Companies are increasingly deploying highly realistic digital human agents (DHAs) controlled by advanced AI for online customer service, tasks typically handled by chatbots. We conducted four experiments to assess users’ perceptions (trustworthiness, affinity, and willingness to work with) and behaviors while using DHAs, utilizing quantitative surveys, qualitative interviews, direct observations, and neurophysiological measurements. Our studies involved four DHAs, including two commercial products (found to be immature) and two future-focused ones (where

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He, Zetong, Lidan Cui, Shunmin Zhang, and Guibing He. "Predicting rock–paper–scissors choices based on single‐trial EEG signals." PsyCh Journal, October 31, 2023. http://dx.doi.org/10.1002/pchj.688.

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AbstractDecision prediction based on neurophysiological signals is of great application value in many real‐life situations, especially in human–AI collaboration or counteraction. Single‐trial analysis of electroencephalogram (EEG) signals is a very valuable step in the development of an online decision‐prediction system. However, previous EEG‐based decision‐prediction methods focused mainly on averaged EEG signals of all decision‐making trials to predict an individual's general decision tendency (e.g., risk seeking or aversion) over a period rather than on a specific decision response in a sin

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