Shireen Mitchell and I talked about Diversity-Friendly Software at SXSW 2017. Here’s the video
And here’s the slides.
http://www.slideshare.net/JonPincus1/diversityfriendly-software-sxsw-2017
We covered a lot! Here a list of references, along with some notes.
Anti-patterns
- 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)
Best Practices
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
Setting intention
- 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
Accessibility
- 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.
Emerging techniques
Gender HCI
- 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
Algorithmic Bias
- 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








I mean, fundamentally my position is the same as Anil Dash’s… Trying to separate out “political” from “apolitical” topics is not going to solve the fucking problem. Being better moderators and not letting people be tremendous bigots even if they’re ‘civil’ are the ways to fix the problem.

Two days after the election, 


