CSCI 15

Exploring Bias in Tech

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Course Description

This course will explore bias in computing at several levels with a focus on how data and technology intersect with and have an impact on different identities in computing. It will examine how bias towards different identities (such as gender, race, ethnicity, socio-economic status, ability, and sexual identity) impacts individuals working in the computing field. It will also examine how these biases can be embedded in data, algorithms, predictive models, and physical products.

The content of this course will roughly be divided into the following three parts:

• Understanding different identities and intersectionality
• Exploring the history and status quo of diversity, inclusion and equity in Computer Science (both in academia and industry)
• Exploring bias in algorithms, prediction models, and technological products We plan to spend roughly 4 class meetings on each of the above three parts.

Policies

Course Syllabus

Part 1: Understanding different identities and intersectionality

• Identity Wheel activity
• Things People With Disabilities Wish You Knew
• Disability: Ask Us Anything
• The Curb-Cut Effect
• Scene on Radio podcast: How Race Was Made
• NYTimes Op-Docs: A Conversation With Latinos on Race
• NYTimes Op-Docs: A Conversation With Native Americans on Race
• NYTimes Op-Docs: A Conversation About Growing Up Black
• NYTimes Op-Docs: A Conversation With White People On Race
• I Am Not Your Asian Stereotype by Canwen Xu
• NYTimes Op-Docs: A Conversation With Asian-Americans on Race by Geeta Gandbhir and Michele Stephenson
• The Urgency of Intersectionality by Kimberle Crenshaw
• NY Times Op-Docs: A Conversation with Black Women on Race
• Picture of a Scientist
• What Doctors Should Know About Gender Identityby Kristie Overstreet
• What Kids Need to Learn About Gender and Sexuality by Lindsay Amer
• University of California San Francisco LGBT Resource Center General Definitions
• Critical Media Project: college students: food+housing insecurity (LA times)
• White Privilege: Unpacking the Invisible Knapsack by Peggy McIntosh

Part 2: Exploring the history and status quo of DEI in Computer Science

• 'The End of Bias' Says There's Hope for Meaningful Change by Jennifer Szalai
• A New Book Examines Ways to End Unconscious Bias by Megan Thompson
• Deconstructing CS Culture by Dr. Amy J. Ko
• The Internet and Racial Justice by Charlton McIlwain
• Jane Margolis - Education, Race, and Computing
• Stuck in the Shallow End: Education, Race, and Computing (Ch. 1: The Myth of Technology as the 'Great Equalizer') by Jane Margolis with Rachel Estrella, Joanna Goode, Jennifer Jellison Holme and Kimberly Nao
• Women and Minorities in Tech, By the Numbers by Ben Bours
• Check Your Stats: The Lack Of Diversity In Venture Capital Is Worse Than It Looks by Elizabeth Edwards
• RateMyInvestor diversity report
• Stereotype Threat: A Conversation with Claude Steele
• Inclusive CS Teaching: Reducing Stereotype Threat
• CRA: Change The Face of Computer Science
• Black Girls CODE: She's Got Next in Tech
• Want More Diversity? Some Experts Say Reward C.E.O.s for It by Peter Eavis
• Nasdaq targets board diversity with ‘comply or explain’ plan by Ben Maiden
• Board Diversity Increased in 2021. Some Ask What Took So Long. by Peter Eavis
• New Law Requires Diversity on Boards of California-Based Companies by David A. Bell, Dawn Belt, and Jennifer J. Hitchcock
• Google’s Ideological Echo Chamber: How bias clouds our thinking about diversity and inclusion
• The Google Manifesto Is Part Of A Much Bigger Problem by Liz Elting
• What exactly is a microaggression? by Jenée Desmond-Harris
• Tool: Recognizing Microaggressions and the Messages They Send (UCSC)
• How to Respond to Microaggressions by Hahna Yoon
• Allies, Accomplices, and Saviors: Knowing the Difference to Maximize Impact by Alexandria Love
• Guide to Allyship

Part 3: Exploring bias in algorithms, prediction models, and technological products

• Coded Bias
• Coded Bias: Educational Discussion Guide by Shalini Kantayya
• CGP Grey: How Machines Learn
• Are we automating racism? Algorithms don’t fail everyone equally. by Joss Fong
• Mapping Pretrial Injustice: Bias in Algorithms
• Safiya Noble: Challenging the Algorithms of Oppression
• The Open Mind: Automating Inequality
• Data and Society: Data Feminism
• NCWIT Conversations for Change with Cathy O'Neil: Bad Algorithms and the Ethical Matrix
• The Real Reason Tech Struggles with Algorithmic Bias by Yael Eisenstat
• Group Backed by Top Companies Moves to Combat A.I. Bias in Hiring by Steve Lohr
• Using A.I. to Find Bias in A.I. by Cade Metz
• FTC warns it could crack down on biased AI by Adi Robertson
• Five Artificial Intelligence Insiders in Their Own Words

Other Resources

We thank these groups for sharing their resources with us:

• Cultural Competence in Computing (3C) Fellows Program
• CS-JEDI: Intro to Justice, Equity, Diversity, and Inclusion in Computer Science