Recent developments and applications of machine learning in legal, social and biomedical contexts have raised questions about ethical implications. Even if the algorithms used in such applications are accurate (that is, they capture the main relevant features that distinguish one group from another) how can we assure that they are fair? For exam ple several researchers as well as activists have alerted that COMPAS, a computer program that judges are using in the United States to help them decide whether to give a prisoner a parole, may generate racially biased decisions because black population are over-represented in the data sample used to train the algorithm.
In this paper, we will use key concepts from Dreyfus’ legacy such as his view on skill acquisition, moral intuition or readiness-to-hand as the way humans usually undertake cognitive tasks to face moral decisions. We will draw from his phenomenological account of ethical expertise (Dreyfus and Dreyfus 1990, 1991, 2004) to establish the abilities that an algorithm should include to make ethical inferences. We need such criteria to avoid designing intelligent systems that appear to be prima facie fair but do not meet the standards of (human) experience-based moral competence.
In the first section we will argue the importance of ethical expertise in AI. In the second section, against emotivist or eliminativist accounts, we will defend that ethical expertise does exist and can be philosophically analysed. In the third section we will present Dreyfus theory of ethical expertise.
The fourth section will be devoted to explain the limits of Dreyfus theory. In the fifth section we will discuss the difficulties that AI may face now to implement such an ethicalexpertise system and whether they might be solved in the near future, presenting our conclusion in the last section.
