Inspired by significant technical advancements, a rapidly growing stream of research explores human lay beliefs and reactions surrounding AI tools, which employ algorithms to mimic elements of human intelligence. This literature predominantly documents negative reactions to these tools or the underlying algorithms, often referred to as algorithm aversion or, alternatively, a preference for humans. This article proposes a third interpretation: people may be averse to their labels, but appreciative of their output. This perspective offers three core insights for how we study people\'s reactions to algorithms. Research would benefit from (1) carefully considering the labeling of AI tools, (2) broadening the scope of study to include interactions with these tools, and (3) accounting for their technical configuration.

UNASSIGNED: While 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.
UNASSIGNED: In a randomized controlled experiment, n = 164 participants read six texts, three of which had a moral and three a technological topic (predictor text topic). The alleged author of each text was randomly either labeled \"ChatGPT\" or \"human author\" (predictor authorship). We captured three dependent variables: assessment of author competence, assessment of content quality, and participants\' intention to submit the text in a hypothetical university course (sharing intention). We hypothesized interaction effects, that is, we expected ChatGPT to score lower than alleged human authors for moral topics and higher than alleged human authors for technological topics and vice versa.
UNASSIGNED: We only found a small interaction effect for perceived author competence, p = 0.004, d = 0.40, but not for the other dependent variables. However, ChatGPT was consistently devalued compared to alleged human authors across all dependent variables: there were main effects of authorship for assessment of the author competence, p < 0.001, d = 0.95; for assessment of content quality, p < 0.001, d = 0.39; as well as for sharing intention, p < 0.001, d = 0.57. There was also a small main effect of text topic on the assessment of text quality, p = 0.002, d = 0.35.
UNASSIGNED: These results are more in line with previous findings on algorithm aversion than with algorithm appreciation. We discuss the implications of these findings for the acceptance of the use of LLMs for text composition.

In recent times, ChatGPT has garnered significant interest from the public, sparking a range of reactions that encompass both aversion and appreciation. This paper delves into the paradoxical attitudes of individuals towards ChatGPT, highlighting the simultaneous existence of algorithmic aversion and appreciation. A comprehensive analysis is conducted from the vantage points of psychology and algorithmic decision-making, exploring the underlying causes of these conflicting attitudes from three dimensions: self-performance, task types, and individual factors. Subsequently, strategies to reconcile these opposing psychological stances are proposed, delineated into two categories: flexible coping and inflexible coping. In light of the ongoing advancements in artificial intelligence, this paper posits recommendations for the attitudes and actions that individuals ought to adopt in the face of artificial intelligence. Regardless of whether one exhibits algorithm aversion or appreciation, the paper underscores that coexisting with algorithms is an inescapable reality in the age of artificial intelligence, necessitating the preservation of human advantages.

Thinking about God promotes greater acceptance of Artificial intelligence (AI)-based recommendations. Eight preregistered experiments (n = 2,462) reveal that when God is salient, people are more willing to consider AI-based recommendations than when God is not salient. Studies 1 and 2a to 2d demonstrate across a wide variety of contexts, from choosing entertainment and food to mutual funds and dental procedures, that God salience reduces reliance on human recommenders and heightens willingness to consider AI recommendations. Studies 3 and 4 demonstrate that the reduced reliance on humans is driven by a heightened feeling of smallness when God is salient, followed by a recognition of human fallibility. Study 5 addresses the similarity in mysteriousness between God and AI as an alternative, but unsupported, explanation. Finally, study 6 (n = 53,563) corroborates the experimental results with data from 21 countries on the usage of robo-advisors in financial decision-making.

[This corrects the article DOI: 10.3389/fpsyg.2022.779028.].

People sometimes exhibit a costly preference for humans relative to algorithms, which is often defined as a domain-general algorithm aversion. I propose it is instead driven by biased evaluations of the self and other humans, which occurs more narrowly in domains where identity is threatened and when evaluative criteria are ambiguous.

Resume screening assisted by decision support systems that incorporate artificial intelligence is currently undergoing a strong development in many organizations, raising technical, managerial, legal, and ethical issues. The purpose of the present paper is to better understand the reactions of recruiters when they are offered algorithm-based recommendations during resume screening. Two polarized attitudes have been identified in the literature on users\' reactions to algorithm-based recommendations: algorithm aversion, which reflects a general distrust and preference for human recommendations; and automation bias, which corresponds to an overconfidence in the decisions or recommendations made by algorithmic decision support systems (ADSS). Drawing on results obtained in the field of automated decision support areas, we make the general hypothesis that recruiters trust human experts more than ADSS, because they distrust algorithms for subjective decisions such as recruitment. An experiment on resume screening was conducted on a sample of professionals (N = 694) involved in the screening of job applications. They were asked to study a job offer, then evaluate two fictitious resumes in a 2 × 2 factorial design with manipulation of the type of recommendation (no recommendation/algorithmic recommendation/human expert recommendation) and of the consistency of the recommendations (consistent vs. inconsistent recommendation). Our results support the general hypothesis of preference for human recommendations: recruiters exhibit a higher level of trust toward human expert recommendations compared with algorithmic recommendations. However, we also found that recommendation\'s consistence has a differential and unexpected impact on decisions: in the presence of an inconsistent algorithmic recommendation, recruiters favored the unsuitable over the suitable resume. Our results also show that specific personality traits (extraversion, neuroticism, and self-confidence) are associated with a differential use of algorithmic recommendations. Implications for research and HR policies are finally discussed.

Over the coming years, AI could increasingly replace humans for making complex decisions because of the promise it holds for standardizing and debiasing decision-making procedures. Despite intense debates regarding algorithmic fairness, little research has examined how laypeople react when resource-allocation decisions are turned over to AI. We address this question by examining the role of perceived impartiality as a factor that can influence the acceptance of AI as a replacement for human decision-makers. We posit that laypeople attribute greater impartiality to AI than human decision-makers. Our investigation shows that people value impartiality in decision procedures that concern the allocation of scarce resources and that people perceive AI as more capable of impartiality than humans. Yet, paradoxically, laypeople prefer human decision-makers in allocation decisions. This preference reverses when potential human biases are made salient. The findings highlight the importance of impartiality in AI and thus hold implications for the design of policy measures.

Algorithms have become increasingly relevant in supporting human resource (HR) management, but their application may entail psychological biases and unintended side effects on employee behavior. This study examines the effect of the type of HR decision (i.e., promoting or dismissing staff) on the likelihood of delegating these HR decisions to an algorithm-based decision support system. Based on prior research on algorithm aversion and blame avoidance, we conducted a quantitative online experiment using a 2×2 randomly controlled design with a sample of N = 288 highly educated young professionals and graduate students in Germany. This study partly replicates and substantially extends the methods and theoretical insights from a 2015 study by Dietvorst and colleagues. While we find that respondents exhibit a tendency of delegating presumably unpleasant HR tasks (i.e., dismissals) to the algorithm-rather than delegating promotions-this effect is highly conditional upon the opportunity to pretest the algorithm, as well as individuals\' level of trust in machine-based and human forecast. Respondents\' aversion to algorithms dominates blame avoidance by delegation. This study is the first to provide empirical evidence that the type of HR decision affects algorithm aversion only to a limited extent. Instead, it reveals the counterintuitive effect of algorithm pretesting and the relevance of confidence in forecast models in the context of algorithm-aided HRM, providing theoretical and practical insights.

Previous research has described physicians\' reluctance to use computerized diagnostic aids (CDAs) but has never experimentally examined the effects of not consulting an aid that was readily available. Experiment 1. Participants read about a diagnosis made either by a physician or an auto mechanic (to control for perceived expertise). Half read that a CDA was available but never actually consulted; no mention of a CDA was made for the remaining half. For the physician, failure to consult the CDA had no significant effect on competence ratings for either the positive or negative outcome. For the auto mechanic, failure to consult the CDA actually increased competence ratings following a negative but not a positive outcome. Negligence judgments were greater for the mechanic than for the physician overall. Experiment 2. Using only a negative outcome, we included 2 different reasons for not consulting the aid and provided accuracy information highlighting the superiority of the CDA over the physician. In neither condition was the physician rated lower than when no aid was mentioned. Ratings were lower when the physician did not trust the CDA and, surprisingly, higher when the physician believed he or she already knew what the CDA would say. Finally, consistent with our previous research, ratings were also high when the physician consulted and then followed the advice of a CDA and low when the CDA was consulted but ignored. Individual differences in numeracy did not qualify these results. Implications for the literature on algorithm aversion and clinical practice are discussed.