Algorithmic Bias: On the Implicit Biases of Social Technology

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 nature of this 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 by presenting what I call 'the Proxy Problem'. One reason biases resist revision is that they rely on proxy attributes, seemingly innocuous attributes that correlate with socially-sensitive attributes, serving as proxies for the socially-sensitive attributes themselves. I argue that in both human and algorithmic domains, this problem presents a common dilemma for mitigation: attempts to discourage reliance on proxy attributes risk a tradeoff with judgement accuracy. This problem, I contend, admits of no purely algorithmic solution

Algorithmic Bias: On the Implicit Biases of Social Technology

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 nature of this 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 by presenting what I call 'the Proxy Problem'. One reason biases resist revision is that they rely on proxy attributes, seemingly innocuous attributes that correlate with socially-sensitive attributes, serving as proxies for the socially-sensitive attributes themselves. I argue that in both human and algorithmic domains, this problem presents a common dilemma for mitigation: attempts to discourage reliance on proxy attributes risk a tradeoff with judgement accuracy. This problem, I contend, admits of no purely algorithmic solution

Are Algorithms Value-Free? 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. 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 points. In this paper, I apply arguments from feminist philosophy of science to machine learning programs to make the case that the resources required to respond to these inductive challenges render critical aspects of their design constitutively value-laden. I demonstrate these points specifically in the case of recidivism algorithms, arguing that contemporary debates concerning fairness in criminal justice risk-assessment programs are best understood as iterations of traditional arguments from inductive risk and demarcation, and thereby establish the value-laden nature of automated decision-making programs. Finally, in light of these points, I address opportunities for relocating the value-free ideal in machine learning and the limitations that accompany them

Reference Magnetism and Macro-Naturalism

2011 Bingham winner for best undergraduate essay.Both David Lewis and Ted Sider have used natural properties to solve the issues of Putnam's Model-Theoretic argument. However, both Lewis and Sider also take naturalness to occur on the micro-physical ground floor. In this paper, I look at the problems that arise with micro-naturalism and also possible alternatives to this view for use in reference magnetism.No embarg

Unconscious Perception and Unconscious Bias: Parallel Debates about Unconscious Content

The possibilities of unconscious perception and unconscious bias prompt parallel debates about unconscious mental content. This chapter argues that claims within these debates alleging the existence of unconscious content are made fraught by ambiguity and confusion with respect to the two central concepts they involve: consciousness and content. Borrowing conceptual resources from the debate about unconscious perception, the chapter distills the two conceptual puzzles concerning each of these notions and establishes philosophical strategies for their resolution. It then argues that empirical evidence for unconscious bias falls victim to these same puzzles, but that progress can be made by adopting similar philosophical strategies. Throughout, the chapter highlights paths forward in both debates, illustrates how they serve as fruitful domains in which to study the relationship between philosophy and empirical science, and uses their combined study to further understanding of a general theory of unconscious content

Internet Sexual Imagery Influencing Sexual Attitudes in Young Adults

The purpose of this dissertation was to study group differences in sexual attitudes and behaviors between young adults who have had high exposure to sexual imagery on the internet when compared to those who have significantly lower exposure. This researcher utilized a correlational research design to obtain information on the sexual attitudes and behaviors of 111 young adults through questionnaires that measured exposure to Internet Sexual Imagery (ISI) along with high risk sexual behaviors, sexual compulsivity and sexual permissiveness. There was a significant relationship between exposure to sexual imagery, sexual compulsivity F(1, 98) = .28.27, MSE = .8.84, p < .01, partial η2= .22 and the permissiveness F (1, 98) = 5.6, MSE = 6.7, p = .02, partial η2= .54 while controlling for gender, race, religion and geographical location. There was not a significant relationship, however, between exposure to ISI and engagement in high risk sexual behaviors F(1, 92) = .2, MSE = 3.4, p = .67, partial η2 = .002. Gender, race, religion and geographical location did not have a significant effect in this study

Cognition and the Structure of Bias

Consider three structurally similar cases of social bias. Mary’s application for graduate school in mathematics is rejected by the traditionalist Mr. T, an evaluator who has written a series of books arguing that women have a natural disposition toward being worse at abstract, logical thinking than men. Her application for a different program is rejected by oblivious Ms. O, an evaluator who avows egalitarian principles but finds that Mary just seems less suitable for the program, for reasons that go unarticulated and would not pan out under pressure. Her application for a third program is rejected by Hal, an automated program that is trained on past admittance data about which students, when accepted, have gone on to successful careers in the field.This dissertation argues that there is a natural kind social bias that all three cases fall under and defends a theory of what that kind is. My theory explains how the cases are unified, how they differ, and why the differences between the cases matter. Within a computational theory of mind, the tasks of unification and differentiation can appear to be at odds with one another. The more we highlight differences among how Mr. T, Ms. O, and Hal were processing informational states, the harder it is to use those same computational resources to say what they have in common. My analysis reconciles these tasks within a cognitive science framework by shifting to a higher level of abstraction.I argue that social bias is a functionally defined mental entity that takes propositional mental states as inputs and returns propositional mental states as outputs in a way that mimics inductions made on the basis of social kind membership. All three cases of bias relate the input that Mary is a woman to the output that she’s not suitable for a mathematics program. Like functional analyses of other mental states, my analysis of bias entails that it is multiply realizable by a variety of computational systems and decision-making processes. For instance, biases could be realized by an explicit belief that women are ill-suited for mathematics (as Mr. T has), by an unconscious, automatic association between women and the stereotypical property of being bad at math (as Ms. O has), or as patterns in how informational states are organized, even when those states are not about specific values or stereotypes, but instead reflect systematic patterns in how our society is organized (as is happening in the case of Hal). Throughout the dissertation, 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