CSAI: Week 8

Lecture 15: Guest Lecture by Zee Talat (PDF)

Title: Evaluating and Mitigating Biases in Machine Learning

Abstract: While research on identifying and mitigating biases is common across machine learning-related fields, there are large gaps both in terms of the amount of work in different fields, the areas that have been considered. For example, natural language processing (NLP) has devoted significant attention to the problem, computer vision (CV) less so, and speech research even less. However, even in fields with a large number of publications dedicated to the identification and mitigation, it is not clear that the research is usable. For instance, even if a mitigation method is proposed, is it clear how to use it? I’ll argue that the majority of bias metrics, i.e., methods for estimating bias in models, in NLP are not actionable because they do not provide potential users with sufficient information to reliably act on their outcomes.

Lecture 16: Case Study Week -- The Environmental Impact of Generating Images with AI (PDF)

Finish readings before attending the session!

Required: Making an image with generative AI uses as much energy as charging your phone

Required: How much water does AI consume? The public deserves to know