Journal Moral Philosophy (forthcoming)

Authors
Gabbrielle Johnson
New York University
Abstract
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.
Keywords fairness  algorithmic bias  feminist philosophy of science  machine learning  underdetermination  inductive risk  values in science  value-free ideal
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References found in this work BETA

The Structure of Scientific Revolutions.Thomas S. Kuhn - 1962 - Chicago: University of Chicago Press.
The Structure of Scientific Revolutions.Thomas S. Kuhn - 1962 - University of Chicago Press.
Fact, Fiction, and Forecast.Nelson Goodman - 1955 - Harvard University Press.
Knowledge and Practical Interests.Jason Stanley - 2005 - Oxford University Press.
Science, Policy, and the Value-Free Ideal.Heather Douglas - 2009 - University of Pittsburgh Press.

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Citations of this work BETA

Oppressive Things.Shen-yi Liao & Bryce Huebner - 2021 - Philosophy and Phenomenological Research 103 (1):92-113.

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