Possible Usage of Sentiment Analysis for Calculating Vectors of Felific Calculus

Abstract-In this paper we introduce an algorithm for affective reasoning based on Bentham's Felific Calculus known also as the hedonic calculus. Knowledge recquired for the task is retrived from a blog corpus by means of sentiment analysis on sentences containing an action or state input. This approach allows a machine to gather information on how usually other people feel when something happens, why people did it and what could happen after the act. Such knowledge is important for understanding actions of others, and for acquiring emphatic skills by a machine. In addition to emotion categorization of Nakamura, we introduce two lexicons based on McDougall's instinct classification and Kohlbergian stages of moral development, then show some basic efficiency of the retrieved knowledge

Breaking bad news in the era of artificial intelligence and algorithmic medicine: an exploration of disclosure and its ethical justification using the hedonic calculus

An appropriate ethical framework around the use of Artificial Intelligence (AI) in healthcare has become a key desirable with the increasingly widespread deployment of this technology. Advances in AI hold the promise of improving the precision of outcome prediction at the level of the individual. However, the addition of these technologies to patient–clinician interactions, as with any complex human interaction, has potential pitfalls. While physicians have always had to carefully consider the ethical background and implications of their actions, detailed deliberations around fast-moving technological progress may not have kept up. We use a common but key challenge in healthcare interactions, the disclosure of bad news (likely imminent death), to illustrate how the philosophical framework of the 'Felicific Calculus' developed in the eighteenth century by Jeremy Bentham, may have a timely quasi-quantitative application in the age of AI. We show how this ethical algorithm can be used to assess, across seven mutually exclusive and exhaustive domains, whether an AI-supported action can be morally justified