We argue that the evolutionary function of reasoning is to allow us to secure more accurate beliefs and more effective intentions through collective deliberation. This sets our view apart both from traditional intellectualist accounts, which take the evolutionary function to be individual deliberation, and from interactionist accounts such as the one proposed by Mercier and Sperber, which agrees that the function of reasoning is collective but holds that it aims to disseminate, rather than come up with, accurate beliefs. We argue (...) that our collectivized intellectualism offers the best explanation of the range of biases that human reasoning is prone to, and that it does better than interactionism at offering a function of reasoning that would have been adaptive for our distant ancestors who first evolved this capacity. (shrink)

We argue that machine learning algorithms can inflict microaggressions on members of marginalized groups and that recognizing these harms as instances of microaggressions is key to effectively addressing the problem. The concept of microaggression is also illuminated by being studied in algorithmic contexts. We contribute to the microaggression literature by expanding the category of environmental microaggressions and highlighting the unique issues of moral responsibility that arise when we focus on this category. We theorize two kinds of algorithmic microaggression, stereotyping and (...) erasure microaggressions, and argue that corporations are responsible for the microaggressions their algorithms create. As a case study, we look at the problems faced by Google’s autocomplete prediction and at the insufficiency of their solutions. The case study of autocomplete demonstrates our core claim that microaggressions constitute a distinct form of algorithmic bias and that identifying them as such allows us to avoid seeming solutions that recreate the same kinds of harms. Google has a responsibility to make information freely available, without exposing users to degradation. To fulfill its duties to marginalized groups, Google must abandon the fiction of neutral prediction and instead embrace the liberatory power of suggestion. (shrink)

Belief has a special connection to truth, a connection not shared by mental states like imagination. One way of capturing this connection is by the claim that belief aims at truth. Normativists argue that we should understand this claim as a normative claim about belief – beliefs ought to be true. A second important connection between belief and truth is revealed by the transparency of belief, i.e. the fact that, when I deliberate about what to believe, I can settle this (...) deliberation only by appeal to considerations I take to show p to be true. It is natural to think that there is a connection between these two features of belief, that the fact that believing for non-evidential considerations would be irrational can help to explain why it is impossible, and Shah and Velleman make exactly this argument. However, as I shall argue, we cannot explain transparency on the basis of a normative requirement on belief. For this explanation to work non-evidential considerations would have to fail to be reasons for belief, and we would have to be able to explain why we are unable to form beliefs on the basis of non-evidential considerations by appealing to the fact that they fail to be reasons for belief. However, while it is plausible that non-evidential considerations are not in fact reasons for belief, the explanatory picture is the other way around. Such considerations only fail to be reasons for belief because we are unable to form beliefs on their basis. (shrink)