I made a playlist of 11 short videos (most are 6-13 mins long) on Ethics in Machine Learning. This is from my ethics lecture in Practical Deep Learning for Coders v4. I thought these short videos would be easier to watch, share, or skip around.
What are Ethics and Why do they Matter? Machine Learning Edition: Through 3 key case studies, I cover how people can be harmed by machine learning gone wrong, why we as machine learning practitioners should care, and what tech ethics are.
All machine learning systems need ways to identify & address mistakes. It is crucial that all machine learning systems are implemented with ways to correctly surface and correct mistakes, and to provide recourse to those harmed.
The Problem with Metrics, Feedback Loops, and Hypergrowth: Overreliance on metrics is a core problem both in the field of machine learning and in the tech industry more broadly. As Goodhart’s Law tells us, when a measure becomes the target, it ceases to be a good measure, yet the incentives of venture capital push companies in this direction. We see out-of-control feedback loops, widespread gaming of metrics, and people being harmed as a result.
Not all types of bias are fixed by diversifying your dataset. The idea of bias is often too general to be useful. There are several different types of bias, and different types require different interventions to try to address them. Through a series of cases studies, we will go deeper into some of the various causes of bias.