Derek Leben

Two recent papers have presented experimental evidence against the hypothesis that there is a semantic connection between OUGHT and CAN, rather than a pragmatic and defeasible one. However, there are two flaws with their designs. One is temporal ambiguity: just asking whether “x ought to A” is underspecified as to _when_ the obligation exists. Another is failing to distinguish between _prior_ obligations and _all-things-considered_ obligations. To test these potential confounds, the chapter author ran two experiments. The first paired some of the original stories with a visual timeline specifying the time of the obligation. The second flipped the wording of the original “obligated but can’t” question into the reversed: “can’t, but still obligated.” In both experiments, there were large and significant differences between the original and modified conditions. These results undermine the conclusions of the previous experiments and remain consistent with the Semantic Hypothesis.

Faculty who teach AI Ethics can learn a lot from those teaching Business Ethics (and vice-versa). This paper provides a survey of a course designed for MBA students, Ethics and AI, exploring the ways in which traditional debates about responsible corporate practices can frame ethical challenges in the responsible design and deployment of AI systems. The course is organized by ethical issue, roughly tracking the flow of information through a model, from data to impacts. Topics include: (1) data ownership, (2) transparency, (3) discrimination, (4) fairness, (5) safety and liability. The course employs normative theories—Utilitarianism, Contractarianism, Libertarianism, and Virtue Ethics—to help students criticize and defend corporate policies and decisions. Ultimately, the goal is to train students in criticizing and defending decisions around the use of AI in products and operations, rather than thinking about AI in a vacuum.

We aim to develop a form of debunking argument according to which an agent’s belief is undermined if the reasons she gives in support of her belief are best explained as rationalizations. This approach is a more sophisticated form of what Shaun Nichols has called best-explanation debunking, which he contrasts with process debunking, i.e., debunking by means of showing that a belief has been generated by an epistemically defective process. In order to develop our approach, we identify an example of such a best-explanation debunking argument in Joshua Greene’s attack on deontology. After showing that this argument is not an instance of process debunking, we offer our best-explanation approach as a generalization of Greene’s argument. Finally, we defend our approach by showing that it is not susceptible to some criticisms that Nichols has leveled against a less sophisticated form of best-explanation debunking.

Ethics for Robots describes and defends a method for designing and evaluating ethics algorithms for autonomous machines, such as self-driving cars and search and rescue drones. Derek Leben argues that such algorithms should be evaluated by how effectively they accomplish the problem of cooperation among self-interested organisms, and therefore, rather than simulating the psychological systems that have evolved to solve this problem, engineers should be tackling the problem itself, taking relevant lessons from our moral psychology. Leben draws on the moral theory of John Rawls, arguing that normative moral theories are attempts to develop optimal solutions to the problem of cooperation. He claims that Rawlsian Contractarianism leads to the 'Maximin' principle - the action that maximizes the minimum value - and that the Maximin principle is the most effective solution to the problem of cooperation. He contrasts the Maximin principle with other principles and shows how they can often produce non-cooperative results. Using real-world examples - such as an autonomous vehicle facing a situation where every action results in harm, home care machines, and autonomous weapons systems - Leben contrasts Rawlsian algorithms with alternatives derived from utilitarianism and natural rights libertarianism. Including chapter summaries and a glossary of technical terms, Ethics for Robots is essential reading for philosophers, engineers, computer scientists, and cognitive scientists working on the problem of ethics for autonomous systems.

Autonomous vehicles must be programmed with procedures for dealing with trolley-style dilemmas where actions result in harm to either pedestrians or passengers. This paper outlines a Rawlsian algorithm as an alternative to the Utilitarian solution. The algorithm will gather the vehicle’s estimation of probability of survival for each person in each action, then calculate which action a self-interested person would agree to if he or she were in an original bargaining position of fairness. I will employ Rawls’ assumption that the Maximin procedure is what self-interested agents would use from an original position, and then show how the Maximin procedure can be operationalized to produce unique outputs over probabilities of survival.

This paper attempts to specify the conditions under which a psychological explanation can undermine or debunk a set of beliefs. The focus will be on moral and religious beliefs, where a growing debate has emerged about the epistemic implications of cognitive science. Recent proposals by Joshua Greene and Paul Bloom will be taken as paradigmatic attempts to undermine beliefs with psychology. I will argue that a belief p may be undermined whenever: (i) p is evidentially based on an intuition which (ii) can be explained by a psychological mechanism that is (iii) unreliable for the task of believing p; and (iv) any other evidence for belief p is based on rationalization. I will also consider and defend two equally valid arguments for establishing unreliability: the redundancy argument and the argument from irrelevant factors. With this more specific understanding of debunking arguments, it is possible to develop new replies to some objections to psychological debunking arguments from both ethics and philosophy of religion.

This paper attempts to specify the conditions under which a psychological explanation can undermine or debunk a set of beliefs. The focus will be on moral and religious beliefs, where a growing debate has emerged about the epistemic implications of cognitive science. Recent proposals by Joshua Greene and Paul Bloom will be taken as paradigmatic attempts to undermine beliefs with psychology. I will argue that a belief p may be undermined whenever: (i) p is evidentially based on an intuition which (ii) can be explained by a psychological mechanism that is (iii) unreliable for the task of believing p; and (iv) any other evidence for belief p is based on rationalization. I will also consider and defend two equally valid arguments for establishing unreliability: the redundancy argument and the argument from irrelevant factors. With this more specific understanding of debunking arguments, it is possible to develop new replies to some objections to psychological debunking arguments from both ethics and philosophy of religion

It is by now a well-supported hypothesis in cognitive neuroscience that there exists a functional network for the moral appraisal of situations. However, there is a surprising disagreement amongst researchers about the significance of this network for moral actions, decisions, and behavior. Some researchers suggest that we should uncover those ethics [that are built into our brains ], identify them, and live more fully by them, while others claim that we should often do the opposite, viewing the cognitive neuroscience of morality more like a science of pathology. To analyze and evaluate the disagreement, this paper will investigate some of its possible sources. These may include theoretical confusions about levels of explanation in cognitive science, or different senses of ‘morality’ that researchers are looking to explain. Other causes of the debate may come from empirical assumptions about how possible or preferable it is to separate intuitive moral appraisal from moral decisions. Although we will tentatively favor the ‘Set Aside’ approach, the questions outlined here are open areas of ongoing research, and this paper will be confined to outlining the position space of the debate rather than definitively resolving it

Linguistic theories of lexical semantics support a Neoclassical Theory of concepts, where entities like CAUSE, STATE, and MANNER serve as necessary conditions for the possession of individual event concepts. Not all concepts have a neoclassical structure, and whether or not words participate in regular linguistic patterns such as verbal alternations will be proposed as a probe for identifying whether their corresponding concepts do indeed have such structure. I show how the Neoclassical Theory supplements existing theories of concepts and supports a version of analyticity and conceptual analysis

Moral Internalism proposes a necessary link between judging that an action is right/wrong and being motivated to perform/avoid that action. Internalism is central to many arguments within ethics, including the claim that moral judgments are not beliefs, and the claim that certain types of moral skepticism are incoherent. However, most of the basis for accepting Internalism rests on intuitions that have recently been called into question by empirical work. This paper further investigates the intuitions behind Internalism. Three experiments show not only that these intuitions are not widespread, but that they are significantly influenced by normative evaluations of the situation in question. These results are taken to undermine Internalist intuitions, and contribute to the growing body of evidence showing that normative evaluations influence supposedly non-normative judgments