Grote, Thomas and Buchholz, Oliver (2025) Shaking up the dogma: Solving trade-offs without (moral) values in machine learning. [Preprint]
![[img]](https://philsci-archive.pitt.edu/style/images/fileicons/text.png) |
Text
Tradeoffs-TGOB.pdf Download (416kB) |
Abstract
The field of machine learning intricately links ethical and epistemological considerations in many contexts which raises the question as to their precise relation. This paper tries to provide a partial answer by focusing on one particular context, namely, the trade-off between accuracy and interpretability, which can be considered a prime example for the entanglement of ethics and epistemology in machine learning. At its core, the trade-off states that any choice of a machine learning model needs to balance the conflicting desiderata of achieving accurate predictions and an interpretable functionality. On a widely shared view inspired by the argument from inductive risk, this balancing of conflicting desiderata can only be resolved by appeal to non-epistemic values. By contrast, we argue that, in certain settings, the accuracy-interpretability trade-off can be resolved on purely epistemic grounds. To that end, we closely analyze the general nature of trade-offs as well as the notions of accuracy and interpretability. This allows us to derive strategies for resolving the accuracy-interpretability trade-off that center around choosing the right epistemic frame for a given machine learning application and, thus, do not require non-epistemic considerations. We conclude by sketching the implications of this result for the general relation of ethical and epistemological considerations in ML.
| Export/Citation: | EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL |
| Social Networking: |
| Item Type: | Preprint | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Creators: |
|
|||||||||
| Keywords: | machine learning; trade-offs; accuracy; interpretability; values in science: | |||||||||
| Subjects: | Specific Sciences > Artificial Intelligence > AI and Ethics Specific Sciences > Artificial Intelligence > Machine Learning |
|||||||||
| Depositing User: | Thomas Grote | |||||||||
| Date Deposited: | 13 Jan 2025 13:41 | |||||||||
| Last Modified: | 13 Jan 2025 13:41 | |||||||||
| Item ID: | 24530 | |||||||||
| Subjects: | Specific Sciences > Artificial Intelligence > AI and Ethics Specific Sciences > Artificial Intelligence > Machine Learning |
|||||||||
| Date: | 12 January 2025 | |||||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/24530 |
Monthly Views for the past 3 years
Monthly Downloads for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[feed]](http://philsci-archive.pitt.edu/style/images/feed-icon-32x32.png){kind=icon}