In this paper, I discuss the nature and consequences of confabulation about political opinions and behaviours. When people confabulate, they give reasons for their choices or behaviour which are ill-grounded and do not capture what really brought the behaviour about, but they do this with no intention to deceive and endorse their own accounts. I suggest that this can happen when people are asked why they voted a certain way, or support certain campaigns, and so on. Confabulating in these political (...) contexts seems bad because we do not get a fully truthful account of why some political choice was made, and so the reasoning behind the choice is under-scrutinised. However, I argue that if people have a virtue of self-regulation, confabulation in political contexts can actually be part of the process of coming to better understand our political choices and embody more consistently the political values which we ascribe to. (shrink)

Other people in our culture actively transform our behavioral dispositions and mental states by shaping them in various ways. In the following, we highlight three points which Veissière et al. may consider in leveraging their account to illuminate the dynamics by which this occurs, and in particular, to shed light on how social cognition supports, and is supported by, enculturation.

Letheby’s book is an engaging and crystal-clear exploration of the philosophical issues raised by the use of psychedelic drugs. In this paper, we focus on the epistemological issues Letheby examines in chapter 8 and argue that his analysis requires an agency-first approach to epistemic evaluation. On an agency-first approach, epistemic evaluation is about identifying the skills agents needs to acquire in order to pursue and fulfil their epistemic goals.

Many artificial intelligence (AI) systems currently used for decision-making are opaque, i.e., the internal factors that determine their decisions are not fully known to people due to the systems’ computational complexity. In response to this problem, several researchers have argued that human decision-making is equally opaque and since simplifying, reason-giving explanations (rather than exhaustive causal accounts) of a decision are typically viewed as sufficient in the human case, the same should hold for algorithmic decision-making. Here, I contend that this argument (...) overlooks that human decision-making is sometimes significantly more transparent and trustworthy than algorithmic decision-making. This is because when people explain their decisions by giving reasons for them, this frequently prompts those giving the reasons to govern or regulate themselves so as to think and act in ways that confirm their reason reports. AI explanation systems lack this self-regulative feature. Overlooking it when comparing algorithmic and human decision-making can result in underestimations of the transparency of human decision-making and in the development of explainable AI that may mislead people by activating generally warranted beliefs about the regulative dimension of reason-giving. (shrink)