And here’s the slides.
We covered a lot! Here a list of references, along with some notes.
- Machine Bias, Julia Angwin et. al., Pro Publica Software issue: algorithmic bias.
- Digital redlining after Trump: Real names and fake news on Facebook, Tressie McMillan Cottom, Medium. Software issues: algorithms manipulable to favor “fake news”; mandatory automated race and gender identification (as opposed to optional self-identification) allows affinity targeting to penalize people identified by the algorithms as “black” and “woman”; reporting mechanism provides tool to harassers.
- Process issue: “real names” violations treated more seriously than violation of racism and sexism community standards.
- Policy issue: “real names” policies harmful to women and other marginalizd groups; see Geek Feminism’s Who is Harmed by a Real Names Policy
- Facebook Lets Advertisers Exclude Users by Race, Julia Angwin and Terry Paris, Jr., Pro Publica. Software issue: mandatory automated race and gender identification (as opposed to optional self-identification)
- Leslie Mac’s Facebook Ban Is The Latest Development In Racially Biased Censorship, Elizabth Adetiba, Black Youth Project. Software issue: reporting mechanism provides tool to harassers.
- How a racist, sexist hate mob forced Leslie Jones off Twitter, Kristen V. Brown, Fusion.
Software issue: functionality gives tools to attackers; lack of tools for people to defend themselves. Policy/process issue: Twitter didn’t enforce their terms of service against attackers.
- “A Honeypot For Assholes”: Inside Twitter’s 10-Year Failure To Stop Harassment, Charlie Warzel, Buzzfeed. Software process issues: not working with the people targeted by harassment means that attempts to deal with the problem haven’t worked; lack of prioritization and investment in a key business problem.
- Airbnb Isn’t Really Confronting Its Racism Problem, Jamie Condliffe, MIT Technology Review.
- Preventing Discrimination at Airbnb, Ben Edelman, benedelman.org. Software issues: unnecessary information (names and photos) enables discrimination; no mechanism for people to test (lack of transparency)
Diverse representation, inclusive culture, equitable policies
- Project Include has recommendations on culture, employee lifecycle , metrics, and more.
- Diversity, Equity, and Inclusion in Science and Technology Action Grid, the White House Office of Science and Technology Policy, November 2016.
- How to build for a diverse and inclusive company, Jon Pincus: a summary of key takeaways from Tech Inclusion 2016
- HOW TO recruit and retain women in tech workplaces, Geek Feminism
- Dreamwidth Diversity Statement
- Django Diversity Statement
- Citizen Code of Conduct from the Stumptown Syndicate
- Adopting a code of conduct is an adaptive challenge not a technical one, Christie Koehler
- Microagressions in Everyday Life, University of Missouri handout
- Alex.js: catch insensitive, inconsiderate writing
- Django primary/replica patch disput
- The W3C’s Web Accessibility Initiative has an Introduction to Web Accessibility, tips on Designing, Writing, and Developing for web accessibility, a summary of the Web Content Accessibility Guidelines as well as the full spec, Authoring Practices, and a lot more info.
- The A11y project: a community-driven effort to make web accessibility easier. Digestible, up-to-date, and forgiving.
- Web accessibility basics by Marco Zehe packs a lot of information into a 50-minute video
Flexible, optional, self-identification
- Male/Female/Othered: Implementing Gender-Inclusiveness in User Data Collection, Finn Harker and Jonathan Ellis, Open Source Bridge 2015
- Disalienation: Why Gender is a Text Field on Diaspora, Sarah Mei
- Genders and Drop-down Menus and Designing a Better Drop-Down Menu for Gender, Sarah Dopp on Dopp Juice.
- The GenderMag Project‘s site includes a downloadable kit and instructions for a gender-specialized cognitive walkthrough and a set of four GenderMag personas.
- GenderMag: A method for evaluating software’s gender inclusiveness and Finding Gender-Inclusiveness Software Issues with GenderMag: A Field Investigation, both by Margaret Burnett et. al., describe this work from a research perspective.
- Are you sure your software is gender-neutral?, Gayna Williams, ACM Interactions
- Gender HCI, Feminist HCI, and Post-Colonial Computing, Jon Pincus, Medium, summarizes research in these areas, and includes several videos
Threat modeling and harassment
- SXSW canceled panels: Here is what happened, Caroline Sinders, Slate, briefly discusses the concept
- A threat model approach to attacks and countermeasures in on-line social networks, Borja Sanz et al., in Proceedings of the 11th Reunion Espanola de Criptografıa y Seguridad de la Información (RECSI)
- OWASP’s Threat Modeling page is a decent introduction to the general topic of threat modeling, although doesn’t apply it to harassment
- Big Risks, Big Opportunities: the Intersection of Big Data and Civil Rights: a White House report
- What does it mean for an algorithm to be fair?, Jeremy Kun
- Big Data, Machine Learning, and the Social Sciences: Fairness, Accountability, and Transparency. Hanna Wallach
- Critical Algorithm Studies: a Reading List, from the Social Media Collective at Microsoft: the literature on algorithms as social processes.
- Fairness in Machine learning, a slide deck from Delip Rao, includes a short reading list
Also published on Medium.