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This chapter questions the way virtue ethics is being drawn into debates about the ethics of social research. In particular, it suggests that discussion of virtue may be motivated by a desire to counter existing, largely principlist, approaches to the ethics of research and its associated administrative structures; virtue ethics has a prima facie appeal for those who are seemingly in need of an alternative moral philosophy. In addition, I argue that, as it stands, the complexity of virtue theory is not fully reflected in, or acknowledged by, debates about the ethics of social research. In the light of these remarks I suggest that the resources of social research can be drawn upon to generate critical theoretical insights into the ethics of social research. I discuss how a normative understanding of practices, and the concept of synderesis understood in a broadly Bourdieuan framework, could provide a starting point for such critical insights. I conclude that this perspective might be taken to suggest that the ethical stance most appropriate to the culture of social research is one of ongoing critical engagement.

This paper aims to identify ethical challenges of using artificial intelligence (AI)-based accounting systems for decision-making and discusses its findings based on Rest's four-component model of antecedents for ethical decision-making. This study derives implications for accounting and auditing scholars and practitioners.

This research is rooted in the hermeneutics tradition of interpretative accounting research, in which the reader and the texts engage in a form of dialogue. To substantiate this dialogue, the authors conduct a theoretically informed, narrative (semi-systematic) literature review spanning the years 2015–2020. This review's narrative is driven by the depicted contexts and the accounting/auditing practices found in selected articles are used as sample instead of the research or methods.

In the thematic coding of the selected papers the authors identify five major ethical challenges of AI-based decision-making in accounting: objectivity, privacy, transparency, accountability and trustworthiness. Using Rest's component model of antecedents for ethical decision-making as a stable framework for our structure, the authors critically discuss the challenges and their relevance for a future human–machine collaboration within varying agency between humans and AI.

This paper contributes to the literature on accounting as a subjectivising as well as mediating practice in a socio-material context. It does so by providing a solid base of arguments that AI alone, despite its enabling and mediating role in accounting, cannot make ethical accounting decisions because it lacks the necessary preconditions in terms of Rest's model of antecedents. What is more, as AI is bound to pre-set goals and subjected to human made conditions despite its autonomous learning and adaptive practices, it lacks true agency. As a consequence, accountability needs to be shared between humans and AI. The authors suggest that related governance as well as internal and external auditing processes need to be adapted in terms of skills and awareness to ensure an ethical AI-based decision-making.