Mark Ryan

As with other fields of applied ethics, philosophers engaged in business ethics struggle to carry out substantive philosophical reflection in a way that mirrors the practical reasoning that goes on within business management itself. One manifestation of the philosopher’s struggle is the field’s division into approaches that emphasize moral philosophy and those grounded in the methods of social science. I claim here that the task for those who come to business ethics with philosophical training is to avoid unintentionally widening the gap between philosophical theory and business management by emphasizing the centrality of practical wisdom to both good managment and to the moral life. Distinguishing my own approach from recent emphases on phronesis in management literature, I draw on the concepts of social practice and of narrative to tie practical reasoning to a company’s unique story. Practical reason, social practices and narrative are employed together to give an account of the art of management at Patagonia. The essay hopes to both provide a way for philosophers engaged with business ethics to see family resemblances between their practices and those of business management and to offer a pedagogical example useful for those in any discipline interested in viewing businesses ethically.

A subdiscipline has emerged around AI ethics, which is comprised of a wide array of individuals: computer scientists, ethicists, cognitive scientists, roboticists, legal professionals, economists, sociologists, gender, and race theorists. This has led to a very interesting branch of research, addressing issues surrounding the development and use of AI. This chapter will give a very brief snapshot of some of the most pertinent ethical concerns. Many of the issues in the Big Data Ethics chapter in this collection are often applicable to AI ethics, because of the data that these technologies retrieve, store, and use, so will not be duplicated here.1 While data-related issues are not new or unique to AI, AI does hold the potential to dramatically retrieve and analyze data that would not be possible otherwise. Data is being used in unique and transformative ways, such as the use of facial recognition to identify individuals from photos or CCTV; AI robots to retrieve live video feed about the patient(s) that it is monitoring; or in new ways, such as self-driving cars collecting an abundance of data about our surroundings, how we drive, and passengers in the car. There is the potential to infringe on individuals’ privacy, restriction of resources, or at worst, the creation of a surveillance society.

Big Data ethics3 informs about how data can be used ethically and gives principles and values to decide how to use it. The values and norms proposed within this area of research aim to influence laws and guide the use of data. The field of Big Data ethics is expanding with new digital technologies and applications, such as the creation of smart cities, robots, or biometric technology, where data fuels their development and innovation. Thus, this field relates to other ethics’ fields – bioethics, ethics of AI, business ethics, Internet ethics, or the ethics of robots. This book chapter has been evaluated through an extensive literature in Big Data ethics (Richards and King 2014) (Boyd and Crawford 2012) (Diaz and Gürses 2012) (Chen and Quan-Haase 2020) (SHERPA 2019) to present a list of the most significant issues in this field.

This paper addresses the instructional challenges of teaching business ethics in a way shaped by Catholic Social Teaching. Focusing on the concept of the Common Good in CST, I describe my use of a case narrative in classroom instruction to help students understand the concept of the Common Good and to perceive the variety of ways businesses can serve or undermine the Common Good in a small city. Through these pedagogical explorations, I illustrate the distinctive vision of business ethics that flows from CST.

While the theory of belief change has attracted a lot of interest from researchers, work on implementing belief change and actually putting it to use in real-world problems is still scarce. In this paper, we present an implementation of propositional belief change using Binary Decision Diagrams. Upper complexity bounds for the algorithm are presented and discussed. The approach is presented both in the general case, as well as on specific belief change operators from the literature. In an effort to gain a better understanding of the empirical efficiency of the algorithms involved, a fault diagnosis problem on combinational circuits is presented, implemented and evaluated.