With their intensive use of data to classify and price risk, insurers have often been confronted with data-related issues of fairness and discrimination. This paper provides a comparative review of discrimination issues raised by traditional statistics versus machine learning in the context of insurance. We first examine historical contestations of insurance classification, showing that it was organized along three types of bias: pure stereotypes, non-causal correlations, or causal effects that a society chooses to protect against, are thus the main sources (...) of dispute. The lens of this typology then allows us to look anew at the potential biases in insurance pricing implied by big data and machine learning, showing that despite utopic claims, social stereotypes continue to plague data, thus threaten to unconsciously reproduce these discriminations in insurance. To counter these effects, algorithmic fairness attempts to define mathematical indicators of non-bias. We argue that this may prove insufficient, since as it assumes the existence of specific protected groups, which could only be made visible through public debate and contestation. These are less likely if the right to explanation is realized through personalized algorithms, which could reinforce the individualized perception of the social that blocks rather than encourages collective mobilization. (shrink)

Firms use algorithms to make important business decisions. To date, the algorithmic accountability literature has elided a fundamentally empirical question important to business ethics and management: Under what circumstances, if any, are algorithmic decision-making systems considered legitimate? The present study begins to answer this question. Using factorial vignette survey methodology, we explore the impact of decision importance, governance, outcomes, and data inputs on perceptions of the legitimacy of algorithmic decisions made by firms. We find that many of the procedural governance (...) mechanisms in practice today, such as notices and impact statements, do not lead to algorithmic decisions being perceived as more legitimate in general, and, consistent with legitimacy theory, that algorithmic decisions with good outcomes are perceived as more legitimate than bad outcomes. Yet, robust governance, such as offering an appeal process, can create a legitimacy dividend for decisions with bad outcomes. However, when arbitrary or morally dubious factors are used to make decisions, most legitimacy dividends are erased. In other words, companies cannot overcome the legitimacy penalty of using arbitrary or morally dubious factors, such as race or the day of the week, with a good outcome or an appeal process for individuals. These findings add new perspectives to both the literature on legitimacy and policy discussions on algorithmic decision-making in firms. (shrink)

Online retailers are using advances in data collection and computing technologies to “personalize” prices, i.e., offer goods for sale to shoppers at their reservation prices, or the highest price they are willing to pay. In this paper, I offer a criticism of this practice. I begin by putting online personalized pricing in context. It is not something entirely new, but rather a kind of price discrimination, a familiar pricing practice. I then offer a fairness-based argument against it. When an online (...) retailer personalizes prices, it competes unfairly for the social surplus created by a transaction. I defend this argument against objections, and offer a simple remedy: online retailers should either disclose that they are personalizing prices, or stop doing so. (shrink)

Critics of Big Tech often describe ‘surveillance capitalism’ in grim terms, blaming it for all kinds of political and social ills. This article counters this pessimistic narrative, offering a more favorable take on companies like Google, YouTube, and Twitter/X. It argues that the downsides of surveillance capitalism are overstated, while the benefits are largely overlooked. Specifically, the article examines six critical areas: i) targeted advertising, ii) the influence of surveillance capitalism on politics, iii) its impact on mental health, iv) its (...) connection with government surveillance, v) its effects on the rule of law and social trust, and vi) privacy concerns. For each area, it will be argued that concerns about surveillance capitalism are unfounded or exaggerated. The article also explores some benefits of the services provided by these technology companies and concludes with a discussion of the practical implications. Throughout, the article draws on empirical evidence relating to the societal and political impact of digital technologies. (shrink)

Organizations are making massive investments in artificial intelligence, and recent demonstrations and achievements highlight the immense potential for AI to improve organizational and human welfare. Yet realizing the potential of AI necessitates a better understanding of the various ethical issues involved with deciding to use AI, training and maintaining it, and allowing it to make decisions that have moral consequences. People want organizations using AI and the AI systems themselves to behave ethically, but ethical behavior means different things to different (...) people, and many ethical dilemmas require trade-offs such that no course of action is universally considered ethical. How should organizations using AI—and the AI itself—process ethical dilemmas where humans disagree on the morally right course of action? Though a variety of ethical AI frameworks have been suggested, these approaches do not adequately address how people make ethical evaluations of AI systems or how to incorporate the fundamental disagreements people have regarding what is and is not ethical behavior. Drawing on moral foundations theory, we theorize that a person will perceive an organization’s use of AI, its data procedures, and the resulting AI decisions as ethical to the extent that those decisions resonate with the person’s moral foundations. Since people hold diverse moral foundations, this highlights the crucial need to consider individual moral differences at multiple levels of AI. We discuss several unresolved issues and suggest potential approaches for thinking about conflicts in moral judgments concerning AI. (shrink)

Interactions between human beings and chatbots are gradually becoming part of our everyday social lives. It is still unclear how human–chatbot interactions, compared to human–human interactions, influence individual morality. Building on the dual-process theory of moral judgment, a secondary data analysis, and two scenario-based experiments provide sufficient evidence that HCIs support utilitarian judgments, which reduce participants' donation amount. Study 3 further demonstrates that the negative effects of HCIs can be attenuated by inducing a social-oriented communication style in chatbots’ verbal language (...) designs. These findings highlight the negative impacts of HCIs on relationships among human beings and suggest a practical intervention for nonprofit organization managers. (shrink)

EM Forster’s short story “The Machine Stops” from 1909 is widely reread and discussed again for some ten years as it portrays a science-fiction world resting on similar technological advancements as today in the digital era. Also management literature reviewed the short story with regard to centralized decision making, rationality and totalitarianism. I argue instead, that the main theme of the short story is – in Forster’s own words – the closing of a civilization in times of transition and facing (...) major challenges. I built the argument by original quotes from Forster and by portraying the years 1906–9, when Forster developed the short story. This era before the Great War starting in 1914 was characterized by euphoric ‘futurism’ based on groundbreaking innovations like ‘long distance messaging’, ‘penny post’, ‘animated films’, Ford’s assembly line, ‘Olivetti typewriter’, ‘feature film’, ‘large ships’ and ‘air transportation’ – the ingredients of the short story as I argue. At the same time these acquitted years were characterized by increasing disintegration, instability, rebellions and a financial crisis with bailout programs. Based on the analogy and as part of speculative philosophy I reconstruct the current great challenges with Forster’ shadow of fatalism and arrive at the urgency to put more effort in addressing and researching pathways out of the crisis and towards stabilization of business and society. (shrink)

Companies increasingly deploy artificial intelligence technologies in their personnel recruiting and selection process to streamline it, making it faster and more efficient. AI applications can be found in various stages of recruiting, such as writing job ads, screening of applicant resumes, and analyzing video interviews via face recognition software. As these new technologies significantly impact people’s lives and careers but often trigger ethical concerns, the ethicality of these AI applications needs to be comprehensively understood. However, given the novelty of AI (...) applications in recruiting practice, the subject is still an emerging topic in academic literature. To inform and strengthen the foundation for future research, this paper systematically reviews the extant literature on the ethicality of AI-enabled recruiting to date. We identify 51 articles dealing with the topic, which we synthesize by mapping the ethical opportunities, risks, and ambiguities, as well as the proposed ways to mitigate ethical risks in practice. Based on this review, we identify gaps in the extant literature and point out moral questions that call for deeper exploration in future research. (shrink)

Clients may feel trapped into sharing their private digital data with insurance companies to get a desired insurance product or premium. However, private insurance must collect some data to offer products and premiums appropriate to the client’s level of risk. This situation creates tension between the value of privacy and common insurance business practice. We argue for three main claims: first, coercion to share private data with insurers is pro tanto wrong because it violates the autonomous choice of a privacy-valuing (...) client. Second, we maintain that irrespective of being coerced, the choice of accepting digital surveillance by insurers makes it harder for the client to protect his or her autonomy. The violation of autonomy also makes coercing customers into digital surveillance pro tanto morally wrong. Third, having identified an economically plausible process involving no direct coercion by insurers, leading to the adoption of digital surveillance, we argue that such an outcome generates further threats against autonomy. This threat provides individuals with a pro tanto reason to prevent this process. We highlight the freedom dilemma faced by regulators who aim to prevent this outcome by constraining market freedoms and argue for the need for further moral and empirical research on this question. (shrink)

This article proposes a conceptual mapping to outline salient properties and relations that allow for a knowledge transfer from the well-established greenwashing phenomenon to the more recent machinewashing. We account for relevant dissimilarities, indicating where conceptual boundaries may be drawn. Guided by a “reasoning by analogy” approach, the article addresses the structural analogy and machinewashing idiosyncrasies leading to a novel and theoretically informed model of machinewashing. Consequently, machinewashing is defined as a strategy that organizations adopt to engage in misleading behavior (...) about ethical Artificial Intelligence /algorithmic systems. Machinewashing involves misleading information about ethical AI communicated or omitted via words, visuals, or the underlying algorithm of AI itself. Furthermore, and going beyond greenwashing, machinewashing may be used for symbolic actions such as lobbying and prevention of stricter regulation. By outlining diverse theoretical foundations of the established greenwashing domain and their relation to specific research questions, the article proposes a machinewashing model and a set of theory-related research questions on the macro, meso, and micro-level for future machinewashing research. We conclude by stressing limitations and by outlining practical implications for organizations and policymakers. (shrink)

Merchants often use personalized pricing: they charge different consumers different prices for the same product. We assess the ethicality of personalized pricing by generalizing and extending an earlier model by Coker and Izaret (Journal of Business Ethics 173:387–398, 2021) who found that price personalization ethically outperforms unitary pricing. Using a simulation analysis, we show that these results crucially depend on the choice of parameters and do not hold universally. We further incorporate additional sources of marginal cost into the utility function (...) that will likely arise from personalized pricing. These include the expectation that personalized pricing is widely considered unfair by consumers who prefer that all consumers are charged the same price (unitary pricing), and that firms often approximate the consumers’ willingness-to-pay in ways that may raise negative sentiments among consumers who feel that their privacy is breached. By extending our model with disutility from unfairness perception and disutility from surveillance aversion, we demonstrate that personalized pricing is quickly outperformed by unitary pricing under social welfare functions that tend to prioritize total utility (utilitarianism and prioritarianism), whereas personalized pricing can ethically outperform unitary pricing under social welfare functions that tend to prioritize equality (egalitarianism and leximin). Our findings illustrate various intricacies and dynamics regarding the circumstances under which personalized pricing can be considered ethical. (shrink)

The renaissance of Aristotelian virtue ethics has produced an extensive philosophical literature that criticizes markets for a lack of virtues. Drawing on Michael Sandel’s virtue-ethical critique of price gouging during natural disasters, we (1) identify and clarify serious misunderstandings in recurring price-gouging debates between virtue-ethical critics and economists. Subsequently, (2) we respond to Sandel’s call for interdisciplinary dialogue. However, instead of solely calling on economics to embrace insights from virtue ethics, we prefer a two-sided version of interdisciplinary dialogue and argue (...) that virtue ethics should embrace economic insights. In particular, we argue that if virtue ethics is to preserve its social relevance under modern conditions, it should re-conceptualize its notion of virtue and re-evaluate the self-interested but effective—and in this sense solidary—help among strangers via markets as virtuous rather than devaluate it as greed, that is, as vicious price gouging. (shrink)