Reading List Source

The course reading will consist of a subset of this list.

Race, Gender and Class in Tech

1. Race, Rigor, and Selectivity in U.S. Engineering (Book)
2. Black Software: The Internet & Racial Justice, from the AfroNet to Black Lives Matter (Book)
3. Silicon Valley has its own long-standing systemic racial problem
4. There Is a Supply of Diverse Workers in Tech, So Why Is Silicon Valley So Lacking in Diversity?
5. The Tech Industry’s Gender-Discrimination Problem
6. Why Is Silicon Valley So Awful to Women?
7. Why Tech’s Approach to Fixing Its Gender Inequality Isn’t Working
8. Hacking Tech’s Diversity Problem
9. Born for it: How the image of software developers came about
10. Elephant in the Valley
11. Silent Technical Privilege
12. Stop Acting So Surprised: How Microaggressions Enforce Stereotypes in Tech
13. Challenging Technical Privilege: How Race and Gender Matter (Symposium)
14. Why Are Some STEM Fields More Gender Balanced Than Others?
15. Pinterest and the Subtle Poison of Sexism and Racism in Silicon Valley
16. Why Silicon Valley’s Many Asian Americans Still Feel Like a Minority

Race, Gender and Class in the Workplace

1. The Case Against Racial Colorblindness
2. Looking Up and Looking Out: Career Mobility Effects of Demographic Similarity among Professionals
3. 6 Steps to Building a Better Workplace for Black Employees
4. NEW IMPLICIT BIAS PRESENTATIONS
5. Making Diversity Work: 7 Steps for Defeating Bias in the Workplace (Book)
6. Diversity Resistance in Organizations (Book)
7. Managing Diversity: Toward a Globally Inclusive Workplace (Book)
8. We Just Can’t Handle Diversity
9. Why Most Performance Evaluations Are Biased, and How to Fix Them
10. Microinequities: When Small Slights Lead to Huge Problems in the Workplace
11. How Hard Should You Push Diversity?
12. How Mindfulness Helped a Workplace Diversity Exercise
13. What If? Short Stories to Spark Diversity Dialogue (Book)

Race, Gender and Class in Academia

1. Diversity Gaps in Computer Science
2. Gender and Ethnic Bias in Letters of Recommendation
3. What’s in a Name: Exposing Gender Bias in Student Ratings of Teaching
4. Examples of Bias in Letters to Watch Out For
5. How Sexism in Tech Impacts the World Outside Silicon Valley
6. Closing the Achievement Gap in Higher Education: An Organizational Learning Perspective
7. “Those invisible barriers are real”: The Progression of First-Generation Students Through Doctoral Education
8. When First Generation Students go to Graduate School
9. ON MENTORING FIRST GENERATION AND GRADUATE STUDENTS OF COLOR
10. Differences in words used to describe racial and gender groups in Medical Student Performance Evaluations
11. Teaching Evals: Bias and Tenure
12. Strategies to improve equity in faculty hiring
13. Gender, Productivity, and Prestige in Computer Science Faculty Hiring Networks
14. Racial and gender biases plague postdoc hiring
15. Structural causes of inequities in STEM hiring and promising strategies for increasing diversity (Presentation)
16. Science faculty’s subtle gender biases favor male students
17. Stuck in the Shallow End: Education, Race, and Computing (Book)
18. What it means to be black in the American educational system
19. Black, Brown, Bruised How Racialized STEM Education Stifles Innovation (Book)

Inclusive STEM/Tech Pedagogy

1. Harvey Mudd College took on gender bias and now more than half its computer-science majors are women
2. Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math
3. Reflecting on the Impact of a Course on Inclusive Strategies for Teaching Computer Science
4. Are College Lectures Unfair?

Broader Impact of Tech

1. 5G, Smart Cities & Communities of Color
2. Racial Equity Impact Assessment Toolkit
3. ACLU: Ban Face Recognition Technology for Law Enforcement
4. IBM Calls for an End of Facial Recognition Technology Over Racial Profiling Fears
5. Algorithms of Oppression How Search Engines Reinforce Racism
6. Language from police body camera footage shows racial disparities in officer respect
7. If Done Right, AI Could Make Policing Fairer
8. A large-scale analysis of racial disparities in police stops across the United States
9. Activists Urge Lawmakers to End Amazon Ring Partnerships With Police
10. Dreaming: Holding Onto the Hope of Justice in Technology and America
11. Layers of Bias: A Unified Approach for Understanding Problems With Risk Assessment
12. Beyond Bias: Re-imagining the Terms of “Ethical AI” in Criminal Law
13. DISCRIMINATING SYSTEMS Gender, Race, and Power in AI
14. Your Computer Is on Fire (Book)
15. Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass (Book)
16. Race After Technology: Abolitionist Tools for the New Jim Code (Book)
17. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor (Book)
18. Invisible Women: Data Bias in a World Designed for Men (Book)
19. Algorithms of Oppression How Search Engines Reinforce Racism (Book)

Tech Ethics and Citizenship

1. How Google treats Meredith Whittaker is important to potential AI whistleblowers
2. The complex world of whistleblowers in tech
3. The Whistleblower’s Dilemma: Do the Risks Outweigh the Benefits?
4. Google’s Project Nightingale highlights the necessity of data science ethics review
5. Technology Ethics Cases
6. Grounding Data Science in a Politics of Justice

International Students

1. International Students in Transition: Voices of Chinese Doctoral Students in a U.S. Research University
2. International Student’s Challenge and Adjustment to College
3. Most difficult problems for Chinese students in American Universities