•  619
    As inductive decision-making procedures, the inferences made by machine learning programs are subject to underdetermination by evidence and bear inductive risk. One strategy for overcoming these challenges is guided by a presumption in philosophy of science that inductive inferences can and should be value-free. Applied to machine learning programs, the strategy assumes that the influence of values is restricted to data and decision outcomes, thereby omitting internal value-laden design choice p…Read more
  •  89
    The Structure of Bias
    Mind 129 (516): 1193-1236. 2020.
    What is a bias? Standard philosophical views of both implicit and explicit bias focus this question on the representations one harbours, for example, stereotypes or implicit attitudes, rather than the ways in which those representations are manipulated. I call this approach representationalism. In this paper, I argue that representationalism taken as a general theory of psychological social bias is a mistake, because it conceptualizes bias in ways that do not fully capture the phenomenon. Crucia…Read more
  •  111
    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 disconcerting sense in which sources of bias emerge out of seemingly innocuous patterns of information processing. The emergent natu…Read more
  •  39
    Cognition and the Structure of Bias
    Dissertation, University of California, Los Angeles. 2019.
    I argue that there exists a natural kind social bias that subsumes seemingly heterogenous cases of implicit bias and other forms of social cognition. I explore the implications of this explicated notion of bias for the organization of the mind, theories of consciousness, and the system-dependence of biases.