A dominant view of guilt and shame is that they have opposing action tendencies: guilt- prone people are more likely to avoid or overcome dysfunctional patterns of behaviour, making amends for past misdoings, whereas shame-prone people are more likely to persist in dysfunctional patterns of behaviour, avoiding responsibility for past misdoings and/or lashing out in defensive aggression. Some have suggested that addiction treatment should make use of these insights, tailoring therapy according to people’s degree of guilt-proneness versus shame-proneness. In this (...) paper, we challenge this dominant view, reviewing empirical findings from others as well as our own to question (1) whether shame and guilt can be so easily disentangled in the experience of people with addiction, and (2) whether shame and guilt have the opposing action tendencies standardly attributed to them. We recommend a shift in theoretical perspective that explains our main finding that both emotions can be either destructive or constructive for recovery, depending on how these emotions are managed. We argue such management depends in turn on a person’s quality of self-blame (retributive or ‘scaffolding’), impacting upon their attitude towards their own agency as someone with fixed and unchanging dispositions (shame and guilt destructive for recovery) or as someone capable of changing themselves (shame and guilt productive for recovery). With an eye to therapeutic intervention, we then explore how this shift in attitude towards the self can be accomplished. Specifically, we discuss empathy-driven affective and narratively-driven cognitive components of a process that allow individuals to move away from the register of retributive self-blame into a register of scaffolding ‘reproach’, thereby enabling them to manage their experiences of both shame and guilt in a more generative way. (shrink)

This paper investigates the nature of dispositional properties in the context of artificial intelligence systems. We start by examining the distinctive features of natural dispositions according to criteria introduced by McGeer (2018) for distinguishing between object-centered dispositions (i.e., properties like ‘fragility’) and agent-based abilities, including both ‘habits’ and ‘skills’ (a.k.a. ‘intelligent capacities’, Ryle 1949). We then explore to what extent the distinction applies to artificial dispositions in the context of two very different kinds of artificial systems, one based on rule-based (...) classical logic and the other on reinforcement learning. Here we defend three substantive claims. First, we argue that artificial systems are not equal in the kinds of dispositional properties they instantiate. In particular, we show that logical systems instantiate merely object-centered dispositions whereas reinforcement learning systems allow for the instantiation of agent-based abilities. Second, we explore the similarities and differences between the agent-centered abilities of artificial systems and those of humans, especially as relates to the important distinction made in the human case between habits and skills/intelligent capacities. The upshot is that the agent-centered abilities of truly intelligent artificial systems are distinctive enough to constitute a third type of agent-based ability — blended agent-based ability — raising substantial questions as to how we understand the nature of their agency. Third, we explore one aspect of this problem, focussing on whether systems of this type are properly considered ‘responsible agents’, at least in some contexts and for some purposes. The ramifications of our analysis will turn out to be directly relevant to various ethical concerns of artificial intelligence. (shrink)