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Philosophy of science at sea: Clarifying the interpretability of machine learning

Beisbart, Claus and Räz, Tim (2022) Philosophy of science at sea: Clarifying the interpretability of machine learning. Philosophy Compass.

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Abstract

In computer science, there are efforts to make machine learning more interpretable or explainable, and thus to better understand the underlying models, algorithms, and their behavior. But what exactly is interpretability, and how can it be achieved? Such questions lead into philosophical waters because their answers depend on what explanation and understanding are—and thus on issues that have been central to the philosophy of science. In this paper, we review the recent philosophical literature on interpretability. We propose a systematization in terms of four tasks for philosophers: (i) clarify the notion of interpretability, (ii) explain the value of interpretability, (iii) provide frameworks to think about interpretability, and (iv) explore important features of it to adjust our expectations about it.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Beisbart, ClausClaus.Beisbart@philo.unibe.ch
Räz, Timtim.raez@gmail.com
Additional Information: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, https://creativecommons.org/licenses/by-nc-nd/4.0/
Subjects: Specific Sciences > Computer Science
General Issues > Computer Simulation
General Issues > Explanation
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Models and Idealization
General Issues > Technology
Depositing User: Tim Räz
Date Deposited: 28 Oct 2022 13:38
Last Modified: 28 Oct 2022 13:38
Item ID: 21316
Journal or Publication Title: Philosophy Compass
Publisher: John Wiley & Sons Ltd
Official URL: https://compass.onlinelibrary.wiley.com/doi/full/1...
DOI or Unique Handle: https://doi.org/10.1111/phc3.12830
Subjects: Specific Sciences > Computer Science
General Issues > Computer Simulation
General Issues > Explanation
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Models and Idealization
General Issues > Technology
Date: 19 April 2022
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/21316

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