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This paper investigates how the introduction of AI to decision making increases moral distance and recommends the ethics of care to augment the ethical examination of AI decision making. With AI decision making, face-to-face interactions are minimized, and decisions are part of a more opaque process that humans do not always understand. Within decision-making research, the concept of moral distance is used to explain why individuals behave unethically towards those who are not seen. Moral distance abstracts those who are impacted by the decision and leads to less ethical decisions. The goal of this paper is to identify and analyze the moral distance created by AI through both proximity distance (in space, time, and culture) and bureaucratic distance (derived from hierarchy, complex processes, and principlism). We then propose the ethics of care as a moral framework to analyze the moral implications of AI. The ethics of care brings to the forefront circumstances and context, interdependence, and vulnerability in analyzing algorithmic decision making. [ABSTRACT FROM AUTHOR]

This paper lays out the conceptual groundwork for a long-term project examining ethical issues raised when addressing the value of knowledge to a knowledge economy. The project includes a series of papers on specific topics that interrelate to the subjects of knowledge, ethics and organizations. While some of the planned articles for the project will have a practical focus, others, such as this one, will be conceptual in nature. The following outlines selected key concepts for an ethics of knowledge and their relationship with cognate areas of inquiry and practice. [ABSTRACT FROM AUTHOR]

Research on AI ethics tends to examine the subject through philosophical, legal, or technical perspectives, largely neglecting the sociocultural one. This literature also predominantly focuses on Europe and the United States. Addressing these gaps, this article explores how data scientists justify and explain the ethics of their algorithmic work. Based on a pragmatist social analysis, and of 60 semi-structured interviews with Israeli data scientists, we ask: how do data scientists understand, interpret, and depict algorithmic ethics? And what ideologies, discourses, and worldviews shape algorithmic ethics? Our findings point to three dominant moral logics: (1) ethics as a personal endeavor; (2) ethics as hindering progress; and (3) ethics as a commodity. We show that while data science is a nascent profession, these moral logics originate from the techno-libertarian culture of its parent profession—engineering. Finally, we discuss the potential of these moral logics to mature into a more formal, agreed-upon moral regime. [ABSTRACT FROM AUTHOR]

Background: The continuous development of artificial intelligence (AI) and increasing rate of adoption by software startups calls for governance measures to be implemented at the design and development stages to help mitigate AI governance concerns. Most AI ethical design and development tools mainly rely on AI ethics principles as the primary governance and regulatory instrument for developing ethical AI that inform AI governance. However, AI ethics principles have been identified as insufficient for AI governance due to lack of information robustness, requiring the need for additional governance measures. Adaptive governance has been proposed to combine established governance practices with AI ethics principles for improved information and subsequent AI governance. Our study explores adaptive governance as a means to improve information robustness of AI ethical design and development tools. We combine information governance practices with AI ethics principles using ECCOLA, a tool for ethical AI software development at the early developmental stages. Aim: How can ECCOLA improve its robustness by adapting it with GARP® IG practices? Methods: We use ECCOLA as a case study and critically analyze its AI ethics principles with information governance practices of the Generally Accepted Recordkeeping principles (GARP®). Results: We found that ECCOLA's robustness can be improved by adapting it with Information governance practices of retention and disposal. Conclusions: We propose an extension of ECCOLA by a new governance theme and card, #21. [ABSTRACT FROM AUTHOR]