Societies change over time. Chattel slavery and foot-binding have been abolished, democracy has become increasingly widespread, gay rights have become established in some countries, and the animal rights movement continues to gain momentum. Do these changes count as moral progress? Is there such a thing? If so, how should we understand it? These questions have been receiving increasing attention from philosophers, psychologists, biologists, and sociologists in recent decades. This survey provides a systematic account of recent developments in the understanding of (...) moral progress. We outline the concept of moral progress and describe the different types of moral progress identified in the literature. We review the normative criteria that have been used in judging whether various developments count as morally progressive or not. We discuss the prospects of moral progress in the face of challenges that claim that moral progress is not psychologically possible for human beings, and we explore the metaethical implications of moral progress. (shrink)

In this dissertation, I construct scientifically and practically adequate moral analogs of cognitive heuristics and biases. Cognitive heuristics are reasoning “shortcuts” that are efficient but flawed. Such flaws yield systematic judgment errors—i.e., cognitive biases. For example, the availability heuristic infers an event’s probability by seeing how easy it is to recall similar events. Since dramatic events, such as airplane crashes, are disproportionately easy to recall, this heuristic explains systematic overestimations of their probability (availability bias). The research program on cognitive heuristics (...) and biases (e.g., Daniel Kahneman’s work) has been scientifically successful and has yielded useful error-prevention techniques—i.e., cognitive debiasing. I attempt to apply this framework to moral reasoning to yield moral heuristics and biases. For instance, a moral bias of unjustified differences in the treatment of particular animal species might be partially explained by a moral heuristic that dubiously infers animals’ moral status from their aesthetic features. While the basis for identifying judgments as cognitive errors is often unassailable (e.g., per violating laws of logic), identifying moral errors seemingly requires appealing to moral truth, which, I argue, is problematic within science. Such appeals can be avoided by repackaging moral theories as mere “standards-of-interest” (a la non-normative metrics of purportedly right-making features/properties). However, standards-of-interest do not provide authority, which is needed for effective debiasing. Nevertheless, since each person deems their own subjective morality authoritative, subjective morality (qua standard-of-interest and not moral subjectivism) satisfies both scientific and practical concerns. As such, (idealized) subjective morality grounds a moral analog of cognitive biases—namely, subjective moral biases (e.g., committed anti-racists unconsciously discriminating). I also argue that "cognitive heuristic" is defined by its contrast with rationality. Consequently, heuristics explain biases, which are also so defined. However, such contrasting with rationality is causally irrelevant to cognition. This frustrates the presumed usefulness of the kind, heuristic, in causal explanation. As such, in the moral case, I jettison the role of causal explanation and tailor categories solely for contrastive explanation. As such, “moral heuristic” is replaced with "subjective moral fallacy," which is defined by its contrast with subjective morality and explains subjective moral biases. The resultant subjective moral biases and fallacies framework can undergird future empirical research. (shrink)

Standard evolutionary explanations seem unable to account for inclusivist shifts that expand the circle of moral concern beyond strategically relevant cooperators. Recently, Allen Buchanan and Russell Powell have argued that this shows that that evolutionary conservatism – the view that our inherited psychology imposes significant feasibility constraints on how much inclusivist moral progress can be achieved – is unjustified. Secondly, they hold that inclusivist gains can be sustained, and exclusivist tendencies curbed, under certain favorable socio-economic conditions. I argue that Buchanan (...) and Powell concede too much to the evolutionary conservative, because their second point shows that conservatives are right about the first: inclusivist shifts are unrealistic where it matters most, namely under harsh social, political and economic conditions. I suggest two promising strategies for solving this problem. One is to focus on different forms of moral progress to secure the same moral gains. The other is to look beyond possible extensions of our psychological capacities altogether, by providing institutional support that renders them irrelevant. We should bypass, rather than further stretch, the constraints of our evolved psychology to make moral progress possible. (shrink)

Implicit cognition is cognition that happens automatically and (typically) non-consciously. In moral psychology, implicit cognition is almost always understood in terms of dual process models of moral judgment. In this chapter, we address the question whether implicit moral judgment is usefully cashed out in terms of automatic (“type 1”) processes, and what the limitations of this approach are. Our chapter has six sections. In (1), we provide a brief overview of dual process models of domain-general (moral and non-moral) cognition. (2) (...) reviews a recent debate regarding the soundness of dual process models to begin with. Section (3) is about dual process accounts of moral judgment specifically. The last three sections then survey attempts to go beyond a simplistic automatic/controlled (“type 2”) distinction in conceptualizing implicit moral cognition. Recently, three main theoretical approaches have emerged, which all revolve around the questions whether type 2 processing can successfully penetrate implicit moral cognition. First, rational learning approaches highlight how episodes of moral learning can feed back into and shape our moral intuitions (4). Second, so-called triple process models suggest that automatic cognitive outputs can under certain circumstances be reined in by reflective cognition (5). Finally, recent evidence shows that moral reasoning is not as impotent as earlier developments in empirical moral psychology claimed to demonstrate (6). (shrink)

As artificial intelligence (AI) thrives and propagates through modern life, a key question to ask is how to include humans in future AI? Despite human involvement at every stage of the production process from conception and design through to implementation, modern AI is still often criticized for its “black box” characteristics. Sometimes, we do not know what really goes on inside or how and why certain conclusions are met. Future AI will face many dilemmas and ethical issues unforeseen by their (...) creators beyond those commonly discussed (e.g., trolley problems and variants of it) and to which solutions cannot be hard-coded and are often still up for debate. Given the sensitivity of such social and ethical dilemmas and the implications of these for human society at large, when and if our AI make the “wrong” choice we need to understand how they got there in order to make corrections and prevent recurrences. This is particularly true in situations where human livelihoods are at stake (e.g., health, well-being, finance, law) or when major individual or household decisions are taken. Doing so requires opening up the “black box” of AI; especially as they act, interact, and adapt in a human world and how they interact with other AI in this world. In this article, we argue for the application of cognitive architectures for ethical AI. In particular, for their potential contributions to AI transparency, explainability, and accountability. We need to understand how our AI get to the solutions they do, and we should seek to do this on a deeper level in terms of the machine-equivalents of motivations, attitudes, values, and so on. The path to future AI is long and winding but it could arrive faster than we think. In order to harness the positive potential outcomes of AI for humans and society (and avoid the negatives), we need to understand AI more fully in the first place and we expect this will simultaneously contribute towards greater understanding of their human counterparts also. (shrink)