<aside> 📣 Announcements:

• Class on Mar 14 (Tue) will be over zoom due to the winter storm. **** https://ucsb.zoom.us/j/9348852270

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• Instructor: Haewon Jeong
• Classes: Tue/Thu 4:00 PM - 5:50 PM, Phelps 1437
• Office hours: Thu 2:45 PM - 3:45 PM, Harold Frank 3161

Overview

The goal of this graduate-level course is to introduce students to real-world ethical issues in machine learning and interesting research questions around the ethics of machine learning. Tentative topics we cover in this course are divided into 4 modules:

• Module 1: Intro to Ethics of ML
• Module 2: Fairness in ML
• Module 3: Privacy in ML
• Module 4: Accountability in ML

You will read, present and discuss a curated set of papers on these topics. In each class, there will be presentation and discussion of ~2 papers/articles.

Prerequisites: Basic knowledge of machine learning is required. In particular, students are expected to have adequate knowledge of probability, statistics, linear algebra, optimization, and programming skills as it pertains to standard ML topics.

Evaluations

• Everyone has to attend the class and actively participate in the discussions. (15%)
• Everyone has to present a paper at least once. (20%)
• There will be one essay assignment. (15%)
• There will be a final project. More details about the project will be updated later.(Project proposal 20%, Final presentation & report 30%)