PhilSci Archive

How Values Shape the Machine Learning Opacity Problem

Sullivan, Emily (2022) How Values Shape the Machine Learning Opacity Problem. Scientific Understanding and Representation (Eds) Insa Lawler, Kareem Khalifa & Elay Shech. pp. 306-322.

[img]
Preview
Text
Values_Opacity_preprint.pdf

Download (304kB) | Preview

Abstract

One of the main worries with machine learning model opacity is that we cannot know enough about how the model works to fully understand the decisions they make. But how much is model opacity really a problem? This chapter argues that the problem of machine learning model opacity is entangled with non-epistemic values. The chapter considers three different stages of the machine learning modeling process that corresponds to understanding phenomena: (i) model acceptance and linking the model to the phenomenon, (ii) explanation, and (iii) attributions of understanding. At each of these stages, non-epistemic values can, in part, determine how much machine learning model opacity poses a problem.


Export/Citation: EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL
Social Networking:
Share |

Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Sullivan, Emilyeesullivan29@gmail.com0000-0002-2073-5384
Keywords: Values in science; Explanation; understanding; machine learning opacity
Subjects: Specific Sciences > Artificial Intelligence > AI and Ethics
General Issues > Ethical Issues
General Issues > Explanation
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Values In Science
Depositing User: Dr. Emily Sullivan
Date Deposited: 04 Dec 2022 23:56
Last Modified: 04 Dec 2022 23:56
Item ID: 21509
Journal or Publication Title: Scientific Understanding and Representation (Eds) Insa Lawler, Kareem Khalifa & Elay Shech
Publisher: Routledge
Official URL: https://www.routledge.com/Scientific-Understanding...
Subjects: Specific Sciences > Artificial Intelligence > AI and Ethics
General Issues > Ethical Issues
General Issues > Explanation
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Values In Science
Date: 2022
Page Range: pp. 306-322
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/21509

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item