Computational ethics

Awad, Edmond; Levine, Sydney; Anderson, Michael; Anderson, Susan; Conitzer, Vincent; Crockett, Molly J.; Everett, Jim A. C.; Evgeniou, Theodoros; Gopnik, Alison; Jamison, Jennifer R.; Kim, Tae Wan; Liao, S. Matthew; Meyer, Michelle N.; Mikhail, John; Opoku-Agyemang, Kweku A.; Borg, Jana Schaich; Schroeder, Juliana; Sinnott‐Armstrong, Walter; Slavkovik, Marija; Tenenbaum, Josh

doi:10.1016/j.tics.2022.02.009

Cited by 26 publications

(14 citation statements)

References 141 publications

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“…Third, fairness in the context of AI has been primarily examined in the information systems and management literature (e.g., Awad et al 2022; Ekstrand et al 2022; Mehrabi et al 2021). Biases can be introduced in the programming and operation of a system (Herden et al 2021; Rahwan et al 2019), and can be classified as data-to-algorithm or data biases (e.g., measurement, representation and aggregation, and sampling and linking biases), algorithm-to-user or method biases (e.g., emergent and correlational biases) and user-to-data biases (e.g., population, historical, and social biases; Mehrabi et al 2021).…”

Section: Ethics Privacy and Fairness Across The Data And Technology L...mentioning

confidence: 99%

Corporate Digital Responsibility in Service Firms and Their Ecosystems

Wirtz

Hartley

Tarbit

2022

Journal of Service Research

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Digitization, artificial intelligence, and service robots carry serious ethical, privacy, and fairness risks. Using the lens of corporate digital responsibility (CDR), we examine these risks and their mitigation in service firms and make five contributions. First, we show that CDR is critical in service contexts because of the vast streams of customer data involved and digital service technology’s omnipresence, opacity, and complexity. Second, we synthesize the ethics, privacy, and fairness literature using the CDR data and technology life-cycle perspective to understand better the nature of these risks in a service context. Third, to provide insights on the origins of these risks, we examine the digital service ecosystem and the related flows of money, service, data, insights, and technologies. Fourth, we deduct that the underlying causes of CDR issues are trade-offs between good CDR practices and organizational objectives (e.g., profit opportunities versus CDR risks) and introduce the CDR calculus to capture this. We also conclude that regulation will need to step in when a firm’s CDR calculus becomes so negative that good CDR is unlikely. Finally, we advance a set of strategies, tools, and practices service firms can use to manage these trade-offs and build a strong CDR culture.

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Section: Ethics Privacy and Fairness Across The Data And Technology L...mentioning

confidence: 99%

Corporate Digital Responsibility in Service Firms and Their Ecosystems

Wirtz

Hartley

Tarbit

2022

Journal of Service Research

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“…Neural networks trained on big data can now be used in a variety of healthcare-related domains, for instance to issue recommendations regarding resource management in hospitals (bed allocation, ventilators and PPE availability, etc. ), transplant waitlists, and more [ 3 ]. Some surgeries and exploratory exams can now be performed by high-precision robots aided by machine learning ([ 16 , 19 ], and AI systems that issue medical diagnoses and suggest treatments have been around for a while [ 11 , 18 ].…”

Section: The Radically Transformative Effects Of Ai On Capabilitiesmentioning

confidence: 99%

“AI for all” is a matter of social justice

Buccella

2022

AI Ethics

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Artificial intelligence (AI) is a radically transformative technology (or system of technologies) that created new existential possibilities and new standards of well-being in human societies. In this article, I argue that to properly understand the increasingly important role AI plays in our society, we must consider its impacts on social justice. For this reason, I propose to conceptualize AI’s transformative role and its socio-political implications through the lens of the theory of social justice known as the Capability Approach. According to the approach, a just society must put its members in a position to acquire and exercise a series of basic capabilities and provide them with the necessary means for these capabilities to be actively realized. Because AI is re-shaping the very definition of some of these basic capabilities, I conclude that AI itself should be considered among the conditions of possession and realization of the capabilities it transforms. In other words, access to AI—in the many forms this access can take—is necessary for social justice.

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“…Sinnott-Armstrong (2008); van Baar, Chang, and Sanfey (2019); Andrejević et al (2020), there has been rising interests in quantifying moral judgments, e.g. Armstrong and Skorburg (forthcoming); Awad et al (2022). An important gap in literature is how to concretely capture moral judgments' evolution with the decision contexts.…”

Section: Introductionmentioning

confidence: 99%

Local Justice and Machine Learning: Modeling and Inferring Dynamic Ethical Preferences toward Allocations

Chen,

Williams,

Leben

et al. 2023

AAAI

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We consider a setting in which a social planner has to make a sequence of decisions to allocate scarce resources in a high-stakes domain. Our goal is to understand stakeholders' dynamic moral preferences toward such allocational policies. In particular, we evaluate the sensitivity of moral preferences to the history of allocations and their perceived future impact on various socially salient groups. We propose a mathematical model to capture and infer such dynamic moral preferences. We illustrate our model through small-scale human-subject experiments focused on the allocation of scarce medical resource distributions during a hypothetical viral epidemic. We observe that participants' preferences are indeed history- and impact-dependent. Additionally, our preliminary experimental results reveal intriguing patterns specific to medical resources---a topic that is particularly salient against the backdrop of the global covid-19 pandemic.

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Computational ethics

Cited by 26 publications

References 141 publications

Corporate Digital Responsibility in Service Firms and Their Ecosystems

Corporate Digital Responsibility in Service Firms and Their Ecosystems

“AI for all” is a matter of social justice

Local Justice and Machine Learning: Modeling and Inferring Dynamic Ethical Preferences toward Allocations

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