Catholicism and the Natural Law: A Response to Four Misunderstandings

This article responds to four criticisms of the Catholic view of natural law: (1) it commits the naturalistic fallacy, (2) it makes divine revelation unnecessary, (3) it implausibly claims to establish a shared universal set of moral beliefs, and (4) it disregards the noetic effects of sins. Relying largely on the Church’s most important theologian on the natural law, St. Thomas Aquinas, the author argues that each criticism rests on a misunderstanding of the Catholic view. To accomplish this end, the author first introduces the reader to the natural law by way of an illustration he calls the “the ten (bogus) rules.” He then presents Aquinas’ primary precepts of the natural law and shows how our rejection of the ten bogus rules ultimately relies on these precepts (and inferences from them). In the second half of the article, he responds directly to each of the four criticisms.

Taking Principles Seriously: A Hybrid Approach to Value Alignment in Artificial Intelligence

An important step in the development of value alignment (VA) systems in artificial intelligence (AI) is understanding how VA can reflect valid ethical principles. We propose that designers of VA systems incorporate ethics by utilizing a hybrid approach in which both ethical reasoning and empirical observation play a role. This, we argue, avoids committing “naturalistic fallacy,” which is an attempt to derive “ought” from “is,” and it provides a more adequate form of ethical reasoning when the fallacy is not committed. Using quantified model logic, we precisely formulate principles derived from deontological ethics and show how they imply particular “test propositions” for any given action plan in an AI rule base. The action plan is ethical only if the test proposition is empirically true, a judgment that is made on the basis of empirical VA. This permits empirical VA to integrate seamlessly with independently justified ethical principles. This article is part of the special track on AI and Society.

Abductive reasoning on humans/AI interactions in medical contexts

The proposal of this talk is to explain alternatives to obtain ethical reasoning in the humans/AI interactions in medical (especially public health) contexts. One of the ethical problems in AI is the alignment mechanisms between human values and machines automatisms. This research is based on obtaining a system capable to infer from rational human activity in a certain behavior, so it can be captured how a human moves and the way that human beings learn and teach ethical values. One way is mimetic alignment, which are values imitation processes based trough big data preferences analysis, linguistic expressions, etc. However, this approximation commits two mistakes. First, preferences are confused with values, and then the second problem is that naturalistic fallacy is committed. From this point of view, naturalistic fallacy occurs if the research is focused on alignment meaning instead of value one, and the subsequent answer is preference analysis based. Therefore, prescriptions are derivate from descriptions. The chain of reasoning that leads us to commit this fallacy begins with the confusion that values and preferences are equivalent. An alternative proposal is anchored values alignment, which is based on anchoring normative values processes of a machine that has a behavior to interact. Through abductive reasoning, this way of thinking tries to capture the idea that a value is not in any set of things, instead it is some action guiding. The relevance of abduction is its temptative value to project beyond descriptive reasoning as statically one, which it is currently used in works on medical diagnosis precisely for the characteristics that clinical eye needs.