Course Syllabus

Instructor:
eyes robson, eyes.robson@berkeley.edu
Office hours: Mon 3-4p; Thurs 5-6p

Faculty sponsor:
Nilah Ioannidis, nilah@berkeley.edu

Course numbers: (to enroll on CalCentral)
1 hour section: 33440
2 hour section: 33441
(no access code necessary, leave blank!)

Meeting time (zoom): Wednesdays 3:10 to 5:00 pm <link in announcements>
note: lectures will be recorded, although the post-lecture discussion portion may not be. This is a Pass / No Pass course, and attendance will be a small portion of the grade. If you anticipate needing to miss a few classes, feel free to reach out about how to receive makeup credit :) 

Anonymous feedback / suggestions: <feedback link TBA>

Course description: AI is eating the world. Unfortunately, these machine learning models have drawn attention in recent years for automating discrimination and being “biased” — a broad term with many distinct manifestations. Within genomics, a field full of complicated ML, these biases lead to dramatically different health outcomes for persons of different socioeconomic status or ancestry. Neither AI nor genomics can be fully “de-biased” in a single semester, but we hope to provide a detailed overview of existing problems and known frameworks to detect and prevent bias through the emerging field of algorithmic fairness. 

This student-led discussion group (with multiple anticipated guest speakers!) is designed to equip the next generation of researchers with the awareness and structural competencies to address bias, and is divided into two consecutive parts.

Part 1 : Algorithmic Fairness in AI (approx. weeks 1-6, 8)

Part 2 : Fairness and Model Portability in Genomics (approx. weeks 7, 9-15)

CS-focused enrollees may consider the 1-hour enrollment option, while Genomics or Computational Biology may prefer the complete 2-hour option, time permitting. At the end of the course, we hope participants will be able to answer the following in their own terms:

1. What is fairness in machine learning, and what are common causes of un fairness?
2. How can cultural biases impact datasets and modeling assumptions? What kind of approaches can account for this situation?
3. (2-hour option) What parallels exist between algorithmic fairness and model portability in genomics?
4. (2-hour option) How can we account for patterns of genetic variation when analyzing genomic data?
   
   

Primary Texts : Fair ML book --  https://fairmlbook.org/ , Fatal Invention (Roberts, 2011),
Language (Technology) is Power article

Format: We will have two enrollment options for the course to allow for students with time constraints or primarily interested in fairness to participate:

1 hour option: CS-focused enrollees can prioritize readings for days or guest lectures that best match their interests, and will be asked to complete fewer readings. This section may be ideal for CS students/researchers primarily interested in fairness rather than in genomic fairness, or for those with limited time availability.

2 hour option : attendance expected at most discussions -- suitable for those interested in fairness with respect to genomic and healthcare data. The final week will be dedicated to a discussion group of mini-projects and course reflections from 2 hour attendees (1 hour enrollees encouraged to attend!). 

The course will be primarily based around weekly readings, with 55-65 minutes of each discussion dedicated to introducing a topic/research area, and another 35-45 minutes dedicated to active discussion of the papers or presentations of ongoing research. (one 2-hour section per week, with one-unit enrollees occasionally getting out early)

Audience: This course is targeted at upper-level undergraduate and new graduate students (Juniors, Seniors, 1st and 2nd year graduate students). Senior PhD students/researchers may also benefit!


Schedule (subject to change):