Work

Value-laden disciplinary shifts in machine learning

Year: 2020

Type: conference-abstract

Abstract: As machine learning models are increasingly used for high-stakes decision making, scholars have sought to intervene to ensure that such models do not encode undesirable social and political values. However, little attention thus far has been given to how values influence the machine learning discipline as a whole. How do values influence what the discipline focuses on and the way it develops? If undesirable values are at play at the level of the discipline, then intervening on particular models will not suffice to address the problem. Instead, interventions at the disciplinary-level are required.

Source: ACM Conference on Fairness, Accountability, and Transparency (FAccT)

DOI: 10.1145/3351095.3373157

Authors: Ravit Dotan, Smitha Milli

Institutions: University of California System, University of California, Berkeley

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Peace, justice, and strong institutions, Reduced inequalities

Open Access status: green

Funder: National Science Foundation