1.2 Implicit Bias, Reinforcement Learning, and Scaffolded Moral Cognition

Recent data from the cognitive and behavioral sciences suggest that irrelevant features of our environment can often play a role in shaping our morally significant decisions. This isn’t always a bad thing. But our inability to suppress or moderate our reflexive reactions can lead us to behave in ways that diverge from our reflectively held ideals, and to pursue worse options while knowing there are better ones available (Spinoza 2002, 320). Nowhere is this clearer than it is where racial biases persist in those who have adopted egalitarian ideals. My primary aim in this paper is to sketch a computational framework of implicit biases, which can explain both their emergence and their stability in the face of egalitarian ideals; I then use this framework to explain why some strategies for intervening on implicit biases are likely to be more successful than others. I argue that there are plausible ways to adjust our goals and manipulate our local environments to moderate the expression of implicit bias in the short-run, but I also maintain that the dynamic nature of learning and valuation, as well as the impact of stress on cognitive processing, necessitate more comprehensive interventions designed to re-shape the cognitive niche we inhabit. Put bluntly, the freedom to act on what we know to be best can only arise in a world where our reflexive and reflective attitudes are, by their very nature, already aligned. So if we wish to gain control over our implicit biases, we must intervene on the world to which our attitudes are attuned, and do so in a way that instills anti-racist attitudes.