Deep Learning and Ethics
Abstract
This article appears as chapter 21 of Prince (2023, Understanding Deep Learning); a complete draft of the textbook is available here: http://udlbook.com. This chapter considers potential harms arising from the design and use of AI systems. These include algorithmic bias, lack of explainability, data privacy violations, militarization, fraud, and environmental concerns. The aim is not to provide advice on being more ethical. Instead, the goal is to express ideas and start conversations in key areas that have received attention in philosophy, political science, and the broader social sciences.
Cite
@article{arxiv.2305.15239,
title = {Deep Learning and Ethics},
author = {Travis LaCroix and Simon J. D. Prince},
journal= {arXiv preprint arXiv:2305.15239},
year = {2023}
}
Comments
Copyright in this Work has been licensed exclusively to The MIT Press, https://mitpress.mit.edu, which will be releasing the final version to the public in 2023. All inquiries regarding rights should be addressed to The MIT Press, Rights and Permissions Department