(drafts available upon request)
The Structure of Bias (under review)
(drafts available upon request)
The Structure of Bias (under review)
What is a bias? Standard philosophical views of both implicit and explicit bias focus this question on the representations one harbors, e.g., stereotypes or implicit attitudes, rather than the ways in which those representations (or other mental states) are manipulated. I call this approach representationalism. In this paper, I argue that representationalism taken as a general theory of bias is a mistake because it conceptualizes social bias in ways that do not fully capture the phenomenon. Crucially, this view fails to capture a heretofore neglected possibility of bias, one that influences an individual’s beliefs about and actions toward other people, but is, nevertheless, nowhere represented in that individual’s cognitive repertoire. In place of representationalism, I develop a functional account of bias that avoids problems posed toward other non-representationalist approaches by regarding bias as a mental entity that takes propositional mental states as inputs and returns propositional mental states as outputs in a way that instantiates, or at the very least mimics, inferences on the basis of an individual’s social group membership. This functional characterization leaves open which mental states and processes bridge the gap between the inputs and outputs, ultimately highlighting the diversity of candidates that can serve this role.
Algorithmic Bias: on the Implicit Biases of Social Technology (under review)
Often machine learning programs inherit social patterns reflected in their training data without any directed effort by programmers to include such biases. Computer scientists call this algorithmic bias. This paper explores the relationship between machine bias and human cognitive bias. In it, I argue similarities between algorithmic and cognitive biases indicate a horrifying sense in which sources of bias emerge out of seemingly innocuous patterns of information processing. This emergent bias obscures the existence of the bias itself, making it difficult to identify, mitigate, or evaluate using standard resources in epistemology and ethics. I demonstrate these points in the case of mitigation techniques, explaining one reason human implicit biases resist revision: cognitive biases, like machine biases, can rely on proxy attributes. This produces a dilemma for mitigation since attempts to discourage reliance on proxy attributes risk a trade-off with judgement accuracy.
Canons of Algorithmic Inference: Feminist Theoretical Virtues in Machine Learning
As inductive decision-making procedures, the inferences made by machine learning programs are subject to underdetermination by evidence and bear inductive risk. Previous attempts to address these challenges have been guided by the presumption that machine learning processes can and should be formally objective. In doing so, the influence of values has been restricted to data and decision outcomes, thereby ignoring internal value-laden choice points. In this paper, I argue that these efforts rest on a mistake: the resources required to respond to these challenges render machine learning processes essentially value-laden, and thus sanction ethical and socio-historical interventions throughout their production, use, and evaluation. I demonstrate these points in the case of recidivism algorithms, arguing that the adoption of feminist theoretical virtues supports the use of false positive equality as a measure of fairness in order to stymie the ongoing harm to the black community within the criminal justice system.
Bias and the Domain of Consciousness
What is the relationship between consciousness and the architecture of the mind? Historically, philosophers have claimed that there exists an intimate connection between the mental and the conscious, often focusing the question of what it is to be mental around that of what it is to be conscious. If some content is conscious, then it must be explicitly represented—that is, there has to be a state to “bring up” to conscious introspection as we do with ordinary beliefs. If a state is unconscious, it’s typically taken to sit in some encapsulated subpersonal system, putting it “below” the purview of consciousness. This chapter argues that some contents in the form of implicit biases can be unconscious at the personal level, highlighting a new way for a state to be unconscious: if it emerges “above” the level of consciousness.
The Psychology of Bias (forthcoming in An Introduction to Implicit Bias: Knowledge, Justice, and the Social Mind, edited by Erin Beeghly and Alex Madva)
What’s going on in the head of someone with an implicit bias? Psychological and philosophical attempts to answer this question have centered on one of two distinct data patterns displayed in studies of individuals with implicit biases: divergence and rational responsiveness. However, explanations focused on these different patterns provide different, often conflicting answers to the question. In this chapter, I provide a literature review that addresses these tensions in data, method, and theory in depth. I begin by surveying the empirical data concerning patterns of divergence and rational responsiveness. Next, I review the psychological theories that attempt to explain these patterns. Finally, I suggest that tensions in the psychological study of implicit bias highlight the possibility that implicit bias is, in fact, a heterogeneous phenomenon, and thus, future work on implicit bias will likely need to abandon the idea that all implicit biases are underwritten by the same sorts of states and process.
Works in Progress:
Inside the Black Box: on the system-dependence of bias
What states and processes realize a bias function? In this chapter, I argue that social biases are not unique to any particular level of cognitive architecture and that the states and processes that constitute bias functions will depend on the wider psychological system in which they’re embedded. Perceptual social biases, for example, will be constituted by obvious, superficial perceptual attributives, like the perceived lightness of someone’s skin. Theory-based social biases, for another example, will be constituted by a complex inferential pattern that tacitly assumes certain stereotypical properties are causally dependent on some underlying, hidden essence. This analysis of bias has an important practical consequence: since the states and processes they comprise are system-dependent, no one mitigation technique will be universally effective. Our most effective debiasing techniques will be tailored to how mental systems globally operate.
Sources of Social Content in Vision
Contemporary theories of visual content disagree about the attributes that make it into the content of visual perceptions. So-called “sparse” views of perceptual content limit the representational capacities of the visual system to low-level attributives such as shape, size, and color. So-called “rich” views of perceptual content maintain that these capacities are more expansive and include high-level features of distal stimuli such as emotional and mental states, artifact categories, and social kinds. I believe this dispute between rich and sparse views of perceptual content neglects another possible interpretation of data: that the visual system represents low-level properties that serve as proxies, or correlated features, for high-level properties. I argue that this alternative explanation reconciles aspects of both rich and sparse views while achieving many of the predictive and explanatory aims theories of social vision are invoked to serve.