Dehumanization by algorithms raises important issues for business and society. Yet, these issues remain poorly understood due to the fragmented nature of the evolving dehumanization literature across disciplines, originating from colonialism, industrialization, post-colonialism studies, contemporary ethics, and technology studies. This article systematically reviews the literature on algorithms and dehumanization (n = 180 articles) and maps existing knowledge across several clusters that reveal its underlying characteristics. Based on the review, we find that algorithmic dehumanization is particularly problematic for human resource management (...) and the future of work, managerial decision-making, consumer ethics, hard- and soft-law regulation, and basic values, including privacy and consumer rights. From the literature synthesis, we also derive the following definition of algorithmic dehumanization: the act of using algorithms and data in a way that results in the intentional or unintentional treatment of individuals and/or groups as less than fully human, thereby violating human rights, including liberty, equality, and dignity. Ultimately, we present a dehumanization avoidance model that serves as a structured research agenda and practical guide to address the challenges raised by algorithmic dehumanization. Thus, the model indicates promising pathways for future research at the intersection of AI and society and facilitates reflection on organizational processes that support improving corporate capabilities and managerial decision-making to avoid algorithmic dehumanization. (shrink)

This study explored the acceptance of artificial intelligence-based judicial decision-making (AI-JDM) as compared to human judges, focusing on the moral foundations of the cases involved using within-subject experiments. The study found a general aversion toward AI-JDM regarding perceived risk, permissibility, and social approval. However, when cases are rooted in the moral foundation of fairness, AI-JDM receives slightly higher social approval, though the effect size remains small. The study also found that demographic factors like racial/ethnic status and age significantly affect these (...) attitudes. Especially, racial/ethnic minorities showed more social approval in AI-JDM than the racial/ethnic majority (i.e., non-Hispanic whites). These findings show the potential role of machine heuristics in the acceptance of AI-JDM and have practical implications for the implementation of AI in judicial systems, particularly suggesting a cautious approach and underscoring the need to inform the public, especially minority groups, about the limitations and potential biases of AI-JDM. (shrink)

Can nonhuman animals and artificial intelligence (AI) entities be attributed moral agency? The general assumption in the philosophical literature is that moral agency applies exclusively to humans since they alone possess free will or capacities required for deliberate reflection. Consequently, only humans have been taken to be eligible for ascriptions of moral responsibility in terms of, for instance, blame or praise, moral criticism, or attributions of vice and virtue. Animals and machines may cause harm, but they cannot be appropriately ascribed (...) moral responsibility for their behavior. This thesis challenges the conventional paradigm by proposing an alternative approach where moral agency is conceived as the competence to participate in moral responsibility practices. By shifting focus from intra-individual to contextual and socially situated features, this practice-focused approach appears to make the attribution of moral agency to nonhuman animals and AI entities more plausible than commonly assumed. Moreover, considering the current and potential future prevalence of nonhuman animals and AI entities in everyday settings and social contexts, a potential extension of moral agency to such entities could very well transform our social, moral, and legal practices. Hence, this thesis proposes that the attribution or withholding of moral agency to different entities should be carefully evaluated, considering the potential normative implications. (shrink)

While philosophers hold that it is patently absurd to blame robots or hold them morally responsible [1], a series of recent empirical studies suggest that people do ascribe blame to AI systems and robots in certain contexts [2]. This is disconcerting: Blame might be shifted from the owners, users or designers of AI systems to the systems themselves, leading to the diminished accountability of the responsible human agents [3]. In this paper, we explore one of the potential underlying reasons for (...) robot blame, namely the folk's willingness to ascribe inculpating mental states or "mens rea" to robots. In a vignette-based experiment (N=513), we presented participants with a situation in which an agent knowingly runs the risk of bringing about substantial harm. We manipulated agent type (human v. group agent v. AI-driven robot) and outcome (neutral v. bad), and measured both moral judgment (wrongness of the action and blameworthiness of the agent) and mental states attributed to the agent (recklessness and the desire to inflict harm). We found that (i) judgments of wrongness and blame were relatively similar across agent types, possibly because (ii) attributions of mental states were, as suspected, similar across agent types. This raised the question - also explored in the experiment - whether people attribute knowledge and desire to robots in a merely metaphorical way (e.g., the robot "knew" rather than really knew). However, (iii), according to our data people were unwilling to downgrade to mens rea in a merely metaphorical sense when given the chance. Finally, (iv), we report a surprising and novel finding, which we call the inverse outcome effect on robot blame: People were less willing to blame artificial agents for bad outcomes than for neutral outcomes. This suggests that they are implicitly aware of the dangers of overattributing blame to robots when harm comes to pass, such as inappropriately letting the responsible human agent off the moral hook. (shrink)

Despite the rapid adoption of technology in human resource departments, there is little empirical work that examines the potential challenges of algorithmic decision-making in the recruitment process. In this paper, we take the perspective of job applicants and examine how they perceive the use of algorithms in selection and recruitment. Across four studies on Amazon Mechanical Turk, we show that people in the role of a job applicant perceive algorithm-driven recruitment processes as less fair compared to human only or algorithm-assisted (...) human processes. This effect persists regardless of whether the outcome is favorable to the applicant or not. A potential mechanism underlying algorithm resistance is the belief that algorithms will not be able to recognize their uniqueness as a candidate. Although the use of algorithms has several benefits for organizations such as improved efficiency and bias reduction, our results highlight a potential cost of using them to screen potential employees during recruitment. (shrink)

Forms of Artificial Intelligence (AI) are already being deployed into clinical settings and research into its future healthcare uses is accelerating. Despite this trajectory, more research is needed regarding the impacts on patients of increasing AI decision making. In particular, the impersonal nature of AI means that its deployment in highly sensitive contexts-of-use, such as in healthcare, raises issues associated with patients’ perceptions of (un) dignified treatment. We explore this issue through an experimental vignette study comparing individuals’ perceptions of being (...) treated in a dignified and respectful way in various healthcare decision contexts. Participants were subject to a 2 (human or AI decision maker) x 2 (positive or negative decision outcome) x 2 (diagnostic or resource allocation healthcare scenario) factorial design. We found evidence of a “human bias” (i.e., a preference for human over AI decision makers) and an “outcome bias” (i.e., a preference for positive over negative outcomes). However, we found that for perceptions of respectful and dignified interpersonal treatment, it matters more who makes the decisions in diagnostic cases and it matters more what the outcomes are for resource allocation cases. We also found that humans were consistently viewed as appropriate decision makers and AI was viewed as dehumanizing, and that participants perceived they were treated better when subject to diagnostic as opposed to resource allocation decisions. Thematic coding of open-ended text responses supported these results. We also outline the theoretical and practical implications of these findings. (shrink)

The transfer of tasks with sometimes far-reaching implications to autonomous systems raises a number of ethical questions. In addition to fundamental questions about the moral agency of these systems, behavioral issues arise. We investigate the empirically accessible question of whether the imposition of harm by an agent is systematically judged differently when the agent is artificial and not human. The results of a laboratory experiment suggest that decision-makers can actually avoid punishment more easily by delegating to machines than by delegating (...) to other people. Our results imply that the availability of artificial agents could provide stronger incentives for decision-makers to delegate sensitive decisions. (shrink)

Previous research has shown that algorithmic decisions can reflect gender bias. The increasingly widespread utilization of algorithms in critical decision-making domains (e.g., healthcare or hiring) can thus lead to broad and structural disadvantages for women. However, women often experience bias and discrimination through human decisions and may turn to algorithms in the hope of receiving neutral and objective evaluations. Across three studies (N = 1107), we examine whether women’s receptivity to algorithms is affected by situations in which they believe that (...) their gender identity might disadvantage them in an evaluation process. In Study 1, we establish, in an incentive-compatible online setting, that unemployed women are more likely to choose to have their employment chances evaluated by an algorithm if the alternative is an evaluation by a man rather than a woman. Study 2 generalizes this effect by placing it in a hypothetical hiring context, and Study 3 proposes that relative algorithmic objectivity, i.e., the perceived objectivity of an algorithmic evaluator over and against a human evaluator, is a driver of women’s preferences for evaluations by algorithms as opposed to men. Our work sheds light on how women make sense of algorithms in stereotype-relevant domains and exemplifies the need to provide education for those at risk of being adversely affected by algorithmic decisions. Our results have implications for the ethical management of algorithms in evaluation settings. We advocate for improving algorithmic literacy so that evaluators and evaluatees (e.g., hiring managers and job applicants) can acquire the abilities required to reflect critically on algorithmic decisions. (shrink)

Several technological developments, such as self-service technologies and artificial intelligence, are disrupting the retailing industry by changing consumption and purchase habits and the overall retail experience. Although AI represents extraordinary opportunities for businesses, companies must avoid the dangers and risks associated with the adoption of such systems. Integrating perspectives from emerging research on AI, morality of machines, and norm activation, we examine how individuals morally behave toward AI agents and self-service machines. Across three studies, we demonstrate that consumers’ moral concerns (...) and behaviors differ when interacting with technologies versus humans. We show that moral intention is less likely to emerge for AI checkout and self-checkout machines compared with human checkout. In addition, moral intention decreases as people consider the machine less humanlike. We further document that the decline in morality is caused by less guilt displayed toward new technologies. The non-human nature of the interaction evokes a decreased feeling of guilt and ultimately reduces moral behavior. These findings offer insights into how technological developments influence consumer behaviors and provide guidance for businesses and retailers in understanding moral intentions related to the different types of interactions in a shopping environment. (shrink)

We explore aversion to the use of algorithms in moral decision-making. So far, this aversion has been explained mainly by the fear of opaque decisions that are potentially biased. Using incentivized experiments, we study which role the desire for human discretion in moral decision-making plays. This seems justified in light of evidence suggesting that people might not doubt the quality of algorithmic decisions, but still reject them. In our first study, we found that people prefer humans with decision-making discretion to (...) algorithms that rigidly apply exogenously given human-created fairness principles to specific cases. In the second study, we found that people do not prefer humans to algorithms because they appreciate flesh-and-blood decision-makers per se, but because they appreciate humans’ freedom to transcend fairness principles at will. Our results contribute to a deeper understanding of algorithm aversion. They indicate that emphasizing the transparency of algorithms that clearly follow fairness principles might not be the only element for fostering societal algorithm acceptance and suggest reconsidering certain features of the decision-making process. (shrink)

Virtue ethics seems to be a promising moral theory for understanding and interpreting the development and behavior of artificial moral agents. Virtuous artificial agents would blur traditional distinctions between different sorts of moral machines and could make a claim to membership in the moral community. Accordingly, we investigate the “machine question” by studying whether virtue or vice can be attributed to artificial intelligence; that is, are people willing to judge machines as possessing moral character? An experiment describes situations where either (...) human or AI agents engage in virtuous or vicious behavior and experiment participants then judge their level of virtue or vice. The scenarios represent different virtue ethics domains of truth, justice, fear, wealth, and honor. Quantitative and qualitative analyses show that moral attributions are weakened for AIs compared to humans, and the reasoning and explanations for the attributions are varied and more complex. On “relational” views of membership in the moral community, virtuous machines would indeed be included, even if they are indeed weakened. Hence, while our moral relationships with artificial agents may be of the same types, they may yet remain substantively different than our relationships to human beings. (shrink)

Large Language Models (LLMs) enable a future in which certain types of legal documents may be generated automatically. This has a great potential to streamline legal processes, lower the cost of legal services, and dramatically increase access to justice. While many researchers focus on proposing and evaluating LLM-based applications supporting tasks in the legal domain, there is a notable lack of investigations into how legal professionals perceive content if they believe an LLM has generated it. Yet, this is a critical (...) point as over-reliance or unfounded scepticism may influence whether such documents bring about appropriate legal consequences. This study is the necessary analysis of the ongoing transition towards mature generative AI systems. Specifically, we examined whether the perception of legal documents’ by lawyers and law students (n = 75) varies based on their assumed origin (human-crafted vs AI-generated). The participants evaluated the documents, focusing on their correctness and language quality. Our analysis revealed a clear preference for documents perceived as crafted by a human over those believed to be generated by AI. At the same time, most participants expect the future in which documents will be generated automatically. These findings could be leveraged by legal practitioners, policymakers, and legislators to implement and adopt legal document generation technology responsibly and to fuel the necessary discussions on how legal processes should be updated to reflect recent technological developments. (shrink)

Compared to human-operated vehicles, autonomous vehicles offer numerous potential benefits. However, public acceptance of AVs remains low. Using 4 studies, including 1 preregistered experiment, the present research examines the role of trust in AV adoption decisions. Using the Trust-Confidence-Cooperation model as a conceptual framework, we evaluate whether perceived integrity of technology—a previously underexplored dimension of trust that refers to perceptions of the moral agency of a given technology—influences AV policy support and adoption intent. We find that perceived technology integrity predicts (...) adoption intent for AVs and that messages that increase perceived integrity of AV technology result in greater AV adoption intent and policy support. This knowledge can be used to guide communication efforts aimed at increasing public trust in AVs, and ultimately enhance integration of AVs into transport systems. (shrink)

Institutions are increasingly employing algorithms to provide performance feedback to individuals by tracking productivity, conducting performance appraisals, and developing improvement plans, compared to traditional human managers. However, this shift has provoked considerable debate over the effectiveness and fairness of algorithmic feedback. This study investigates the effects of negative performance feedback (NPF) on the attitudes, cognition and behavior of medical researchers, comparing NPF from algorithms versus humans. Two scenario-based experimental studies were conducted with a total sample of 660 medical researchers (algorithm (...) group: N1 = 411; human group: N2 = 249). Study 1 analyzes the differences in scientific misconduct, moral disengagement, and algorithmic attitudes between the two sources of NPF. The findings reveal that NPF from algorithms shows higher levels of moral disengagement, scientific misconduct, and negative attitudes towards algorithms compared to NPF from humans. Study 2, grounded in trait activation theory, investigates how NPF from algorithms triggers individual’s egoism and algorithm aversion, potentially leading to moral disengagement and scientific misconduct. Results indicate that algorithm aversion triggers individuals’ egoism, and their interaction enhances moral disengagement, which in turn leads to increased scientific misconduct among researchers. This relationship is also moderated by algorithmic transparency. The study concludes that while algorithms can streamline performance evaluations, they pose significant risks to scientific misconduct of researchers if not properly designed. These findings extend our understanding of NPF by highlighting the emotional and cognitive challenges algorithms face in decision-making processes, while also underscoring the importance of balancing technological efficiency with moral considerations to promote a healthy research environment. Moreover, managerial implications include integrating human oversight in algorithmic NPF processes and enhancing transparency and fairness to mitigate negative impacts on medical researchers’ attitudes and behaviors. (shrink)

When people interact with social robots, they treat them as real social agents. How people depict robots is fun to consider, but when people are confronted with embodied entities that move and talk – whether humans or robots – they interact with them as authentic social agents with minds, and not as mere representations.

Departing from the claim that AI needs to be trustworthy, we find that ethical advice from an AI-powered algorithm is trusted even when its users know nothing about its training data and when they learn information about it that warrants distrust. We conducted online experiments where the subjects took the role of decision-makers who received advice from an algorithm on how to deal with an ethical dilemma. We manipulated the information about the algorithm and studied its influence. Our findings suggest (...) that AI is overtrusted rather than distrusted. We suggest digital literacy as a potential remedy to ensure the responsible use of AI. (shrink)

Automation is inherently tied to ethical challenges because of its potential involvement in morally loaded decisions. In the present research, participants (n = 34) took part in a moral multi-trial dilemma-based task where the agent (human vs. machine) and the behavior (action vs. inaction) factors were randomized. Self-report measures, in terms of morality, consciousness, responsibility, intentionality, and emotional impact evaluation were gathered, together with electroencephalography (delta, theta, beta, upper and lower alpha, and gamma powers) and peripheral autonomic (electrodermal activity, heart (...) rate variability) data. Data showed that moral schemata vary as a function of the involved decider, and when the agent and behavior factors are crossed. Subjects did not consider machines full moral deciders to the same degree as humans and tend to morally better accept human active behavior and machine inaction. Moreover, the autonomic physiological activity might support the a-posteriori moral evaluation. In the evaluation of the agent’s consciousness, a beta ventrolateral prefrontal synchronization was detected for human action and machine inaction, while a generalized gamma synchronization occurred in artificial agent trials while rating the emotional impact of the decider’s behavior. The detected differences might point to a potential explicit and implicit asymmetry in moral reasoning toward artificial and human agents. (shrink)

This paper concerns top-down approaches in machine ethics. It is divided into three main parts. First, I briefly describe top-down design approaches, and in doing so I make clear what those approaches are committed to and what they involve when it comes to training an AI to behave ethically. In the second part, I formulate two underappreciated motivations for endorsing them, one relating to predictability of machine behavior and the other relating to scrutability of machine decision-making. Finally, I present three (...) major worries about such approaches, and I attempt to show that advocates of top-down approaches have some plausible avenues of response. I focus most of my attention on what I call the ‘technical manual objection’ to top-down approaches, inspired by the work of Annas (2004). In short, the idea is that top-down approaches treat ethical decision-making as being merely a matter of following some ethical instructions in the same way that one might follow some set of instructions contained in a technical manual (e.g., computer manual), and this invites sensible skepticism about the ethical wisdom of machines that have been trained on those approaches. I respond by claiming that the objection is successful only if it is understood as targeting machines that have certain kinds of goals, and it should not compel us to totally abandon top-down approaches. Such approaches could still be reasonably employed to design ethical AI that operate in contexts that include fairly noncontroversial answers to ethical questions. In fact, we should prefer top-down approaches when it comes to those types of context, or so I argue, due to the advantages I claim for them. (shrink)

The current research aims to answer the following question: “who will be held responsible for harm involving an artificial intelligence system?” Drawing upon the literature on moral judgments, we assert that when people perceive an AI system’s action as causing harm to others, they will assign blame to different entity groups involved in an AI’s life cycle, including the company, the developer team, and even the AI system itself, especially when such harm is perceived to be intentional. Drawing upon the (...) theory of mind perception, we hypothesized that two dimensions of mind: perceived agency—attributing intention, reasoning, pursuing goals, and communicating to AI, and perceived experience—attributing emotional states, such as feeling pain and pleasure, personality, and consciousness to AI—mediated the relationship between perceived intentional harm and blame judgments toward AI. We also predicted that people are likely to attribute higher mind characteristics to AI when harm is perceived to be directed to humans than when it is perceived to be directed to non-humans. We tested our research model in three experiments. In all experiments, we found that perceived intentional harm led to blame judgments toward AI. In two experiments, we found perceived experience, not agency, mediated the relationship between perceived intentional harm and blame judgments. We also found that companies and developers were held responsible for moral violations involving AI, with developers received the most blame among the entities involved. Our third experiment reconciles the findings by showing that perceived intentional harm directed to a non-human entity did not lead to increased attributions of mind to AI. These findings have implications for theory and practice concerning unethical outcomes and behavior associated with AI use. (shrink)

Background: Allocation of scarce organs for transplantation is ethically challenging. Artificial intelligence (AI) has been proposed to assist in liver allocation, however the ethics of this remains unexplored and the view of the public unknown. The aim of this paper was to assess public attitudes on whether AI should be used in liver allocation and how it should be implemented. Methods: We first introduce some potential ethical issues concerning AI in liver allocation, before analysing a pilot survey including online responses (...) from 172 UK laypeople, recruited through Prolific Academic. Findings: Most participants found AI in liver allocation acceptable (69.2%) and would not be less likely to donate their organs if AI was used in allocation (72.7%). Respondents thought AI was more likely to be consistent and less biased compared to humans, although were concerned about the “dehumanisation of healthcare” and whether AI could consider important nuances in allocation decisions. Participants valued accuracy, impartiality, and consistency in a decision-maker, more than interpretability and empathy. Respondents were split on whether AI should be trained on previous decisions or programmed with specific objectives. Whether allocation decisions were made by transplant committee or AI, participants valued consideration of urgency, survival likelihood, life years gained, age, future medication compliance, quality of life, future alcohol use and past alcohol use. On the other hand, the majority thought the following factors were not relevant to prioritisation: past crime, future crime, future societal contribution, social disadvantage, and gender. Conclusions: There are good reasons to use AI in liver allocation, and our sample of participants appeared to support its use. If confirmed, this support would give democratic legitimacy to the use of AI in this context and reduce the risk that donation rates could be affected negatively. Our findings on specific ethical concerns also identify potential expectations and reservations laypeople have regarding AI in this area, which can inform how AI in liver allocation could be best implemented. (shrink)

Social development organizations increasingly employ artificial intelligence -enabled tools to help team members collaborate effectively and efficiently. These tools are used in various team management tasks and activities. Based on the unified theory of acceptance and use of technology, this study explores various factors influencing employees’ use of AI-enabled tools. The study extends the model in two ways: a) by evaluating the impact of these tools on the employees’ collaboration and b) by exploring the moderating role of AI aversion. Data (...) were collected through an online survey of employees working with AI-enabled tools. The analysis of the research model was conducted using partial least squares, with a two-step model – measurement and structural models of assessment. The results revealed that the antecedent variables, such as effort expectancy, performance expectancy, social influence, and facilitating conditions, are positively associated with using AI-enabled tools, which have a positive relationship with collaboration. It also concluded a significant effect of AI aversion in the relationship between performance expectancy and use of technology. These findings imply that organizations should focus on building an environment to adopt AI-enabled tools while also addressing employees’ concerns about AI. (shrink)

While algorithmic decision-making (ADM) is projected to increase exponentially in the coming decades, the academic debate on whether people are ready to accept, trust, and use ADM as opposed to human decision-making is ongoing. The current research aims at reconciling conflicting findings on ‘algorithmic aversion’ in the literature. It does so by investigating algorithmic aversion while controlling for two important characteristics that are often associated with ADM: increased benefits (monetary and accuracy) and decreased user control. Across three high-powered (Ntotal = (...) 1192), preregistered 2 (agent: algorithm/human) × 2 (benefits: high/low) × 2 (control: user control/no control) between-subjects experiments, and two domains (finance and dating), the results were quite consistent: there is little evidence for a default aversion against algorithms and in favor of human decision makers. Instead, users accept or reject decisions and decisional agents based on their predicted benefits and the ability to exercise control over the decision. (shrink)

Clark and Fischer (C&F) claim that trait attribution has major limitations in explaining human–robot interactions. We argue that the trait attribution approach can explain the three issues posited by C&F. We also argue that the trait attribution approach is parsimonious, as it assumes that the same mechanisms of social cognition apply to human–robot interaction.