|URL for this page: http://bit.ly/racegenderinsiliconvalley|
CS 80Q: Race & Gender in Silicon Valley
|This is a working draft by Cynthia Lee firstname.lastname@example.org|
|Description: Join us as we go behind the scenes of a year of big headlines about trouble in Silicon Valley. We'll start with the basic questions of who decides who gets to see themselves as "a computer person," and how do early childhood and educational experiences shape our perceptions of our relationship to technology? Then we'll see how those questions are fundamental to a wide variety of recent events from #metoo and the fight against sexual harassment in tech companies, to how the under-representation of women and people of color in tech companies impacts the kinds of products that Silicon Valley brings to market. We'll see how data and the coming age of AI raise the stakes on these questions of identity and technology: Exactly how much do companies like Amazon, Google, and Facebook know about you, and how could that data be used to target you in potentially harmful ways? How can we ensure that AI technology will help reduce bias in human decision-making in areas from marketing to criminal justice, rather than amplify it?|
|Course Goals: This course interrogates the social challenges of Silicon Valley, a place of privilege, privation, and precarity, and encourages students to perform their own ethnographical studies through writing, coding, engagement, digital culture, and social practice. We will learn about the importance of technology in shaping our critical understanding of social conditions in our community and the global economy. (1) Students will learn to beome close and careful analysts of technology and social change; through readings, ethnographic studies, coding projects, field trips and debates. (2) Students will learn how to think critically about technology by comparing ethical frameworks and social criticism and debating the most effective roads to social justice. (3) Students will learn to become skilled interpreters of the social and cultural challenges that Silicon Valley faces with respect to race and gender. (4) Students will learn about how technology interfaces with diversity and the power dynamics of race, class, gender, health status, citizenship status. (5) Students will learn to translate their insights into sustained intellectual discussion and lucid analytical prose.|
|Below is a brainstorm of possible readings. This is far more than can fit in the course, and curation is still in process. I welcome suggestions for addition!|
|See "Syllabus" tab (below) for course topics in calendar format|
Janet Abbate, Recoding Gender : Women’s Changing Participation in Computing
|Emily Chang, Brotopia|
|R. W. Connell, Masculinities|
Virginia Eubanks, Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
|Mar Hicks, Programmed Inequality|
An Xiao Mina, Memes to Movements: How the World's Most Viral Media Is Changing Social Protest & Power (in press)
|Safiya Noble, Algorithms of Oppression||https://www.youtube.com/watch?v=iRVZozEEWlE|
Cathy O'Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
|Ellen Pao, Reset: My Fight for Inclusion and Lasting Change|
David N. Pellow and Lisa Sun-Hee Park, The Silicon Valley of Dreams Environmental Injustice, Immigrant Workers, and the High-Tech Global Economy
|Margot Lee Shetterly, Hidden Figures|
Sara Wachter-Boettcher, Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech
|Leslie Berlin, Troublemakers: Silicon Valley's Coming of Age||https://youtu.be/ZhBLwsRJPFQ|
Hal Abelson, Harry Lewis, and Ken Ledeen; Blown to Bits: Your Life, Liberty, and Happiness After the Digital Explosion
Michelle Alexander, The New Jim Crow: Mass Incarceration in the Age of Colorblindness
|Ijeoma Oluo, "So You Want to Talk About Race"||https://www.youtube.com/watch?v=TnybJZRWipg|
|Paulo Freire, Pedagogy of the Oppressed||(for students who want to create K12 curricula for their final project)|
|Sheila Jasanoff, The Ethics of Invention: Technology and the Human Future|
|Clyde Ford, Think Black: A Memoir||https://www.latimes.com/opinion/story/2019-09-20/ibm-nazi-germany-tech-racism-father|
|Carol Gilligan, In a Different Voice||Ethics of care|
|John Stuart Mill||Utilitarianism/consequentialism|
|Judith Butler||3rd wave feminism|
|Audre Lorde, "Age, race, class, and sex: women redefining difference"||https://www.colorado.edu/odece/sites/default/files/attached-files/rba09-sb4converted_8.pdf|
|Immanuel Kant, “What is Enlightenment”||http://www.columbia.edu/acis/ets/CCREAD/etscc/kant.html|
|John Rawls, "Theory of Justice"||http://www.csus.edu/indiv/c/chalmersk/econ184sp09/johnrawls.pdf|
|D. Soyini Madison, Critical Ethnography: Method, Ethics, and Performance|
|Martin Luther King Jr, “Letter from the Birmingham Jail”||http://www.africa.upenn.edu/Articles_Gen/Letter_Birmingham.html|
|Peggy McIntosh, "White Privilege: Unpacking the Invisible Knapsack"||http://www.racialequitytools.org/resourcefiles/mcintosh.pdf|
|Articles (scholarly, popular press, personal essay)|
A.T. Wynn and S.J. Correll, "Puncturing the pipeline: Do technology companies alienate women in recruiting sessions?"
|Kathryn McKinley, 'What Happens to Us Does Not Happen to Most of You"||https://www.sigarch.org/what-happens-to-us-does-not-happen-to-most-of-you/|
|Shireen Mitchell and Jon Pincus, "Diversity-friendly software at SXSW 2017"|
|Julia Angwin et al., "Machine Bias"||https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing|
|Steve Henn, “When women stopped coding”||https://www.npr.org/sections/money/2014/10/21/357629765/when-women-stopped-coding|
Adrienne LaFrance, “Why Do So Many Digital Assistants Have Feminine Names?”
Voigt et al., “Language from police body camera footage shows racial disparities in officer respect”
|Elora Israni, “When an Algorithm Helps Send You to Prison”||https://www.nytimes.com/2017/10/26/opinion/algorithm-compas-sentencing-bias.html|
The Economist editorial staff, “The e-mail Larry Page should have written to James Damore”
|Cynthia Lee on the Google Memo||https://www.vox.com/the-big-idea/2017/8/11/16130452/google-memo-women-tech-biology-sexism|
Godwin, Potvin, Hazari, Lock “Identity, Critical Agency, and Engineering: An Affective Model for Predicting Engineering as a Career Choice”
Tracy Chou, "“I had so many advantages, and I barely made it”: Pinterest engineer on Silicon Valley sexism"
Ben Williams, "Debugging Code(Switching): My Experiences as a Black Computer Science Student at Stanford University"
Alona King, "No, I Am Not Lost A Black woman’s experience in the Stanford Computer Science Major"
Kaya Thomas, "What it’s Like to Be a Black Woman in Tech: A Q&A with Kaya Thomas"
|Erin Biba, "What It’s Like When Elon Musk’s Twitter Mob Comes After You"||https://www.thedailybeast.com/what-its-like-when-elon-musks-twitter-mob-comes-after-you|
Peter Balonon-Rosen and Lizzie O'Leary, "How women landed the invisible work of social media labor"
Sepehr Vakil, "Ethics, Identity, and Political Vision Toward a Justice-Centered Approach to Equity in Computer Science Education"
Sasha Costanza-Chock, "Design Justice: Towards an Intersectional Feminist Framework for Design Theory and Practice"
Jessica Guynn, "Silicon Valley's race gap is getting worse, not better, new research shows"
Susan Sibley “Why Do So Many Women Who Study Engineering Leave the Field?”
|Noam Cohen, "We're All Nerds Now"||https://www.nytimes.com/2014/09/14/sunday-review/were-all-nerds-now.html|
|Betsy DiSalvo et al. "African American Males Constructing Computing Identity"||http://betsydisalvo.com/wp-content/uploads/2017/11/p2967-disalvo.pdf|
Betsy DisSalvo "Gaming Masculinity: Constructing Masculinity with Video Games"
Kristin A. Searle and Yasmin B. Kafai, "Boys' Needlework: Understanding Gendered and Indigenous Perspectives on Computing and Crafting with Electronic Textiles"
Fred Turner (interview), "Don't Be Evil: Fred Turner on Utopias, Frontiers, and Brogrammers"
Jonathan McIntosh, "Playing with Privilege: the invisible benefits of gaming while male"
Adewole S. Adamson and Avery Smith, "Machine Learning and Health Care Disparities in Dermatology"
Richard F. Martell, David M. Lane, and Cynthia Emrich, "Male-Female Differences: A Computer Simulation"
Ian Gent, "The Petrie Multiplier: Why an Attack on Sexism in Tech is NOT an Attack on Men"
|Michael Feldman et al., "Certifying and removing disparate impact"||https://arxiv.org/abs/1412.3756|
Sorelle A. Friedler et al., "A comparative study of fairness-enhancing interventions in machine learning"
|Danielle Ensign et al., "Decision making with limited feedback"||http://proceedings.mlr.press/v83/ensign18a.html|
|Sorelle A. Friedler et al, "On the (im)possibility of fairness"||https://arxiv.org/abs/1609.07236|
|Moritz Hardt, "How Big Data is Unfair"||https://medium.com/@mrtz/how-big-data-is-unfair-9aa544d739de|
Dale Beran, "Gamergate: Anon Defends his Safe Spaces" (Part 4 of "4chan: The Skeleton Key to the Rise of Trump")
Allison Master and Andrew N. Meltzoff, "Building bridges between psychological science and education: Cultural stereotypes, STEM, and equity"
|Universal principles of data ethics: 12 guidelines for developing ethics codes|
Linda Yang, "Fighting Silicon Valley Sexism as a Queer Woman" interview of Leanne Pittsford
|Chas Danner, "More Than Half of Russian Facebook Ads Focused on Race"|
|Gillian B. White, "When Algorithms Don’t Account for Civil Rights"||https://www.theatlantic.com/business/archive/2017/03/facebook-ad-discrimination/518718/|
S. Cheryan et al. "Ambient belonging: How stereotypical cues impact gender participation in computer science."
Daisuke Wakabayashi "Meet the People Who Train the Robots (to Do Their Own Jobs)"
|Dave Lee, "Why Big Tech pays poor Kenyans to teach self-driving cars"||https://www.bbc.com/news/technology-46055595|
|Kate Losse, "The Male Gazed: Surveillance, Power, and Gender"||https://modelviewculture.com/pieces/the-male-gazed|
Nakamura, Lisa, "Indigenous Circuits: Navajo Women and the Racialization of Early Electronic Manufacture"
|Breaking News During the Class|
|Algorithm bias: Amazon scraps sexist AI resume analyzer|
|Interface bias/Algorithm bias and voting rights||https://www.nytimes.com/2018/10/19/us/politics/north-dakota-voter-identification-registration.html|
|Harassment: Linux kernel open-source project adopots Contributor Covenant||https://www.wired.com/story/woman-bringing-civility-to-open-source-projects/|
|Hostile environment: NIPS conference refuses women's call to change name|
|Role of Facebook ads again under scrutiny in midterm election|
|Algorithm bias: Gmail autocomplete and pronoun bias|