Deep Learning and Ethics

TL;DR

This paper discusses ethical issues in deep learning, including bias, privacy, and societal impacts, aiming to foster dialogue rather than prescribe solutions.

Contribution

It explores ethical challenges in deep learning from interdisciplinary perspectives, highlighting areas needing further discussion and research.

Findings

Identifies key ethical concerns like bias and privacy in AI systems.

Highlights societal and environmental impacts of deep learning.

Encourages interdisciplinary dialogue on AI 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.