Bias in a Biased System: Visual Perceptual Prejudice

The visual system responds to both an excess of available information and an underdetermination problem by relying on priors that facilitate its successful navigation of an environment on the basis of past encounters. These priors can concern demographic features, such as race or gender, in a manner that resembles prejudicial bias. This focuses our attention on the following problem: how can we identify prejudicial bias within a system that relies on a kind of structural bias to accomplish its goals? Taking two bodies of recent empirical work as case studies, I explore and reject three possible criteria for demarcating problematic instances of bias. I identify a principled reason why standard epistemic criteria cannot accomplish the task. I instead propose an adoption of a skill-based model of visual perception that allows for multi-dimensional evaluation relative to a set of potentially competing goals. 0. Bias in a biased system The term ‘bias’ is used in a variety of ways. Two uses are of particular relevance to this chapter. We often use ‘bias’ colloquially to mean a kind of prejudice, against a particular social group. This is the sense in which we talk of “implicit bias” grounding discrimination, or of a system that is unfairly “biased” against a racial minority, for instance. This sense of bias is inherently freighted with a negative valence: it has problematic ethical upshots. In addition, it is often taken to be epistemically flawed, either because it is grounded in accurate information or irrational cognitive processes, or because it perpetuates flawed reasoning downstream of it.1 We can call this kind of bias prejudicial bias. Prejudicial bias generally concerns demographic groups: people of a particular race, gender, sexual orientation, or class. But in science more generally, bias also has a thinner, formal sense, meaning any kind of weighting in a testing process that systematically skews the outcome. This is what we have in mind when we talk of a confirmation bias, for instance, when a process is skewed towards producing results which confirm a previously endorsed hypothesis. Bias in this second sense may sometimes also be prejudicial against particular demographic groups, but it need not be. And some kinds of action recommended to combat the first, negatively valenced kind of bias may be instances of the second, formal sort of bias, such as deliberately assigning readings by female authors on a particular topic to combat their underrepresentation on philosophy syllabi, for 1 For a range of philosophical perspectives on the nature of implicit prejudicial bias and the sorts of problems it can give rise to see Brownstein and Saul (2016). For a discussion of the downstream epistemic problems that prejudicial bias can give rise to see Gendler (2011). Jessie Munton Bias in a biased system 2 instance, or practices of affirmative action. We can call this second kind of bias formal bias.2 Formal bias need not always constitute an epistemic flaw, though it may sometimes do so. And under certain conditions, it can constitute an ecologically rational response, one that maximizes the quantity and quality of information at the disposal of an organism. It is not, therefore, inherently epistemically valenced. Nor is it inherently morally valenced. In this sense, it is a neutral category. What is the relationship between prejudicial bias and formal bias? Is prejudicial bias just an instance of formal bias that happens to have a particular demographic orientation, or is it a category that is marked out itself by further structural flaws in reasoning, for instance? This question has important upshots for our understanding of the epistemic status of prejudicial bias: are the epistemic problems that frequently seem to accompany it merely contingent, or is it in fact distinguished by structural properties which are themselves constitutive of an epistemic flaw? Standard accounts of epistemic normativity have tended to abstract away from the particular content of the beliefs or arguments under evaluation, in favor of focusing on formal requirements they must meet to attain a certain standard, such as knowledge, or justification. Evidentialism for instance, claims that believers must conform their beliefs to the evidence. This requirement applies irrespective of the subject matter of the beliefs in question (Connee and Feldman 2004). Similarly, reliabilism about knowledge or justification emphasizes the significance of accuracy or the truth-ratio of a given means of producing belief (Goldman 1979; Kornblith 2002). This is again a formal requirement: it does not pertain to the contents of the beliefs under scrutiny. We shall call a system of evaluation that can be described and applied without appeal to content a “formal” method of evaluation.3 If prejudicial bias is epistemically flawed as a category, then we would expect it to be distinguished not just by its demographic focus, but in addition by certain structural features in virtue of which those flaws arise, since standard methods of epistemic evaluation are not sensitive to whether or not the content of belief concerns a demographic group. In this chapter I pursue this question, of what distinguishes prejudicial bias from the broader class of formal bias, specifically in the context of the visual system. Visual perception is a process which demonstrates systematic formal bias. It relies on previously-encountered information to guide its accumulation and interpretation of new data. There is also evidence that visual perception can demonstrate a sensitivity to demographic categories including race and gender, in a manner that resembles a 2 See Greenwald and Krieger (2006) for a related distinction between neutral “response bias” and implicit, discriminatory bias. 3 The exception to this general rule is the literature on pragmatic encroachment, the view that what a subject has practically at stake can “encroach” on epistemic standards applied to them (Fantl and McGrath 2002; Hawthorne 2003; Stanley 2005; Weatherson 2012). Higher stakes make it harder to know a given proposition, for instance. Applying this variable standard requires attention to the contents of the belief and details of the believer’s situation. Jessie Munton Bias in a biased system 3 kind of prejudicial bias. 4 This throws up the following question: how are we to identify instances of prejudicial bias within a formally biased system? I argue that there are cases of prejudicial bias which are indistinguishable in formal terms from the standard operation of the visual system. But I also claim that these cases do manifest distinctive epistemic flaws. Understanding how we can square this circle requires a reappraisal of our epistemic evaluation of visual perception more generally. To capture the full range of instances of visual prejudicial bias we need to conceptualize visual perception as an active practice that develops over time, that crucially involves not just transitions between pieces of information but decisions about what information to acquire, and that can consequently only be fully epistemically appraised relative to a goal and an environment. The introduction of these additional parameter gives us the room we need to individuate prejudicial bias in a way that reveals it to be constitutively tied to a distinctive kind of epistemic flaw. In doing so, we make some progress on the broader question of how to identify prejudicial bias within a structurally formally biased system. This chapter proceeds as follows. In the first section I describe how the visual system is both formally biased at a structural level, and capable of engaging in a kind of prejudicial bias. I draw on two bodies of empirical work on face perception, and the resolution of perceptual ambiguity to make this point. In the third section I consider candidate criteria that fail to demarcate prejudicial bias from mere formal bias. In the final section I offer an analysis of why these criteria are bound to fail, and propose a different approach to visual perception, one that emphasizes its status as a skill honed through active practice in a given environment, whose evaluation is only possible relative to a set of goals. 1. Vision as a biased system The visual system faces two major challenges. On the one hand, it has too much information: the information available from the environment at any one moment far surpasses what the brain can process (Summerfield and Egner 2009). On the other hand it has too little information: the 3D world is projected onto the 2D retina in a manner that is essentially ambiguous, and the brain must work to reconstruct a representation of the environment from that impoverished data (Scholl 2005). The solution to both of these problems is similar: the visual system relies on information it has encountered in the past, encoding previous environmental regularities, to let it select those pieces of information most likely to issue an appropriate epistemic reward, and to help it resolve indeterminacy in retinal data. In some sense, the visual 4 We tend to think of prejudicial bias as something that manifests in cognition, and that is, if not the upshot person-level processing, at least something for which an individual may be blamed or held accountable in some sense. I am using prejudicial bias in a thinner sense here, that can apply to the upshot of any level of processing, realized by an individual, a sub-system of an individual, or even a computer algorithm. Jessie Munton Bias in a biased system 4 system is thereby systematically biased, biased towards the interpretation of the novel in line with the familiar, thereby winning greater reliability, (as measure by the increased rate of accuracy of the resulting experiences), at the cost of a loss of sensitivity to surprising data. Bayesian models of visual perception bring out this biased structure. These models propose that the visual system overcomes the