Deep Learning and Ethics
It addresses ethical concerns in AI for researchers and practitioners, but is incremental as it synthesizes existing discussions without new solutions.
The chapter examines potential harms from AI systems, such as algorithmic bias and lack of explainability, aiming to express ideas and spark conversations in ethics-related fields.
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.