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)

Purpose The internet has provided a gamut of benefits to consumers. The digital world, however, also provides space for various illegal or unethical consumer activities. Consumers may not always be fully aware of the unethical or illegal nature of some of the online activities that they engage in. This study aims to examine the questionable side of online consumer behaviour in an emerging market where internet penetration and smart phone accessibility is rapidly expanding. Using a third-person technique, this study attempts (...) to empirically capture the perceptions of Indian adults regarding the prevalence of various questionable online activities such as unauthorized downloading of digital content, spreading fake news/misinformation and fraudulent returns and to understand the extent to which these respondents believe that such actions are acceptable or illegal and unethical. Design/methodology/approach An online questionnaire was used to collect primary data from 212 consumers. Non-probability convenience and snowball sampling was used for the purpose. Findings Unauthorized watching or downloading of online content is reported to be the most prevalent among the various types of questionable behaviours examined. However, it is behaviours such as fraudulent returns and spreading misinformation through online channels which are considered to be the most unethical or illegal. Certain behaviours which may be deemed to be unethical and illegal nevertheless are seen as acceptable. Significant differences between demographics in the case of several of the unethical activities are reported. Research limitations/implications This study examines the grey and dark side of online behaviours among consumers in an emerging market and points to the need for action on several fronts to increase consumer awareness and sensitivity about the unethical or illegal nature of some of their online activities and the implications for multiple stakeholders. Based on the findings of this study, recommendations directed at consumers, marketers and policymakers are discussed. Originality/value Although the benefits of online communication channels have been extensively studied, their ability to facilitate certain unethical and even illegal activities is an under-researched area. The inclination to engage in these types of questionable behaviours may have been exacerbated by the restrictions imposed by the COVID-19 pandemic. This study highlights the importance of research on various grey consumer activities in the digital space and paves the way for further investigations by identifying online actions which are considered as most prevalent and/or unethical and illegal. (shrink)

Businesses increasingly rely on algorithms that are data-trained sets of decision rules (i.e., the output of the processes often called “machine learning”) and implement decisions with little or no human intermediation. In this article, we provide a philosophical foundation for the claim that algorithmic decision-making gives rise to a “right to explanation.” It is often said that, in the digital era, informed consent is dead. This negative view originates from a rigid understanding that presumes informed consent is a static and (...) complete transaction. Such a view is insufficient, especially when data are used in a secondary, noncontextual, and unpredictable manner—which is the inescapable nature of advanced artificial intelligence systems. We submit that an alternative view of informed consent—as an assurance of trust for incomplete transactions—allows for an understanding of why the rationale of informed consent already entails a right to ex post explanation. (shrink)

With the advent of the Internet of Things (IoT), it has become increasingly challenging for users to assess the privacy risks associated with consumer products and the continuous stream of user data needed to operate them. In this study, we propose and test three mechanisms with the potential to help users make more accurate assessments of privacy risks. We refer to these mechanisms as framing (i.e., presenting information on the collection and use of user data with or without direct reference (...) to privacy risks), comparing (i.e., presenting a product and the associated information on data collection and use with or without reference to an alternative product), and educating (i.e., augmenting users’ general privacy literacy). To assess these mechanisms in different IoT contexts, we conducted two scenario-based online experiments with reference to a telematics device ( n = 317) and a fitness tracker ( n = 356). In both studies, we find that actual privacy risks as manipulated in the experiment are only moderately related to the privacy risks perceived by users. However, comparing and educating each helped users make more accurate privacy risk assessments. In Study 2, framing and comparing jointly enabled especially users with low privacy literacy to assess privacy risks more accurately. These findings have meaningful implications for key actors in the IoT ecosystem and those regulating it. (shrink)

When should a company introduce a new ethical norm? This article uses the value-belief-norm theory to argue that the more an ethical issue threatens a company's ethical value and the more the company has an ethical responsibility to protect such value against such threat, then the more desirable it is for a company to establish ethical norms to protect said value. Distinguishing seven characteristics of an ethical issue and four conditions for a company's ethical responsibility helps identify the situations in (...) which a company should introduce a new ethical norm. The resulting framework, with corresponding guidelines, specifies two accelerators and one inhibitor that influence the desirable speed for introducing new norms. (shrink)

In recent years, insurance companies have begun tracking their customers’ behaviors and price premiums accordingly. Based on the Market-Failures Approach as well as the Justice-Failures Approach, I provide an ethical analysis of the use of tracking technologies in the insurance industry. I focus on the use of telematics in car insurance and on the use of fitness tracking in life insurance. The use of tracking has some important benefits to policyholders and insurers alike: it reduces moral hazard and fraud, increases (...) actuarial fairness, and incentivizes safe behavior. These benefits, however, are outweighed by significant moral objections. First, the use of tracking technologies significantly undermines the fairness of the interaction between insurance companies and policyholders. Specifically, the use of tracking eliminates information asymmetries, but in such a way that favors insurers exclusively. Furthermore, tracked behaviors and the ability to choose to behave safely are highly correlated with other variables such as income. Therefore, tracking-based insurance relies on and exacerbates existing inequalities and injustices, which undermines the benefits that tracking is supposed to introduce. (shrink)