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 b...
| Main Author: | |
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| Format: | Electronic Article |
| Language: | English |
| Check availability: | HBZ Gateway |
| Journals Online & Print: | Drawer...
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| Fernleihe: | Fernleihe für die Fachinformationsdienste |
| Published: |
Brill
2024
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| In: |
Journal of moral philosophy
Year: 2024, Volume: 21, Issue: 1/2, Pages: 27-61 |
| Further subjects: | B
values in science
B theoretical virtues B moral encroachment B inductive risk B value-free ideal B algorithmic bias |
| Online Access: |
Presumably Free Access Volltext (lizenzpflichtig) Volltext (lizenzpflichtig) |
| Summary: | 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. |
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| ISSN: | 1745-5243 |
| Contains: | Enthalten in: Journal of moral philosophy
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| Persistent identifiers: | DOI: 10.1163/17455243-20234372 |