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Diversity-friendly software (SXSW 2017)
This document discusses best practices and emerging techniques for creating diversity-friendly software. It addresses three aspects of diversity: the people creating software, the processes used, and supporting diversity in the software itself. Best practices include having diverse representation on teams, an inclusive culture, equitable policies, accessibility design, and optional self-identification. Emerging techniques discussed are gender HCI, threat modeling for harassment, and avoiding algorithmic bias. Challenges covered include lack of priority on diversity and lack of knowledge, and responses provided are to prioritize diversity and budget for training. The document advocates starting with the needs of highly marginalized groups and creating a virtuous cycle of diverse and inclusive people creating empowering software.
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Diversity-friendly software (SXSW 2017)
- 1. Diversity-friendly software Shireen Mitchell- @digitalsista Jon Pincus - @jdp23 SXSW 2017 References: http://bit.ly/div5y-links
- 3.
- 4. Three aspects ofdiversity in software The people and teams that create software The processes we use to create software Supporting diversity in the software itself
- 5. Software today worksbest for people like its creators
- 13. Software reinforces powerdynamics SoftwarePeople and Communities create empowers
- 14.
- 15. Best practices Diverse representation,inclusive culture, equitable policies Setting intention - intersectionally Accessibility Flexible self-identification of gender, race,and pronouns
- 16. Questions: diversity, inclusion,equity How diverse is your team and community? How inclusive is your culture? How equitable is your organization? What are you doing to increase diversity, inclusion, and equity? How committed is the senior leadership - and the entire organization? Who's responsible for it - and do they have the time and resources to make an impact?
- 17. Setting intention -intersectionally Make diversity a priority Educate your team and community Avoid problematic language in products and code
- 18. Intention: Questions toask yourself Is diversity as a goal and a priority? If so, how are you communicating that internally and externally? Do you have a Diversity Statement and a Code of Conduct? Do people in the organization understand microagressions? How are you educating your team and community? Are you encouraging a "call-in" culture? What standards, tools, and processes do you have about problematic language?
- 19. Accessibility Design for peoplewith disabilities from the beginning, including Supporting screen readers and other assistive technologies Keyboard-only navigation Color vision impairment Hard of hearing or deaf Seizure disorders and cognitive disabilities
- 20. Accessibility: Questions toask yourself Are you designing for accessibility from the beginning? Do you have accessibility expertise throughout the team? Do you include accessibility in your testing? Does everybody on the team use the software in accessibility modes? What automated accessibility tools do you use? What are you doing to increase awareness and understanding?
- 21. Flexible, optional self-identification Allowflexibility (not just a fixed list) Allow multiple choices; e.g., somebody who’s multiracial may be black and Latinx Let people decline to answer Avoid the term “other” Let people choose the pronouns they prefer
- 22. Self-identification: Questions toask yourself Do people have a flexible way to specify their race, gender, orientation, ...? Does it support multiracial, asexual, gender-fluid, and other frequently- marginalized people? Is it optional? Can people choose their preferred pronouns? Have you listened to feedback from a diverse group of people?
- 23.
- 24. Emerging Techniques Gender HCI Threatmodeling for harassment Avoiding algorithmic bias
- 25. Gender HCI Gender differencesin human-computer interaction
- 26. Gender HCI: fivekey facets Self-efficacy: how confident are people in their abilities? Information-processing style: start by gathering fairly complete information, or try something promising and backtrack if necessary? Risk aversion: how comfortable are people with risk? Tinkering: how much do people playfully experiment with the software? Motivation: interest in technology for its own sake, or in aid of accomplishing something?
- 27. Gender Mag A gender-specialized cognitivewalkthrough and a set of four personas, for finding gender HCI problems in software http://gendermag.org/
- 28. Threat modeling forharassment Threat modeling is a standard security technique An adversary attacks the systems Defenses prevent attacks Mitigations reduce the effect Harassers = adversaries Work with experts (aka targets) as well as studying patterns of harassment techniques
- 29. Algorithmic biases Analyze algorithmsfor “fairness” What does “fairness” mean in your context? Work with social scientists as well as techies Be wary of biases in training sets and historical data Biases can be race, gender, class, cultural, urban/rural, age, ... Make sure algorithms transparent enough that you can analyze them for fairness
- 30.
- 31. Challenges - andresponses Diversity is typically seen as low priority and divisive Choose to prioritize - in an inclusive way See diversity as an asset to product development Look for ways diversity can give a strategic advantage Diversity failures can have huge financial, PR, and strategic consequences Inability to pivot, expand the audience, exit, ...
- 32. Challenges - andresponses Lack of knowledge about diversity Treat it just like you would any other key skill your organization doesn’t have enough of Budget time and money for training, education, consultants
- 33. Challenges - andresponses Investment patterns favor cis straight white male focused products Look for non-traditional investments (crowdfunding, etc.) We’re seeing more forward-looking decision-makers who get it We need a few breakthrough success as proof points!
- 34.
- 35. Start with thehighly marginalized Women of color Gender-diverse people People with disabilities - including “invisible disabilities” ... It seems easier to start designing for the usual suspects (cis straight white able-bodied techie guys) But that leaves you with a diversity debt
- 36. Start with thehighly marginalized There are great designers and developers from marginalized communities out there Get them on your team - as leaders Bring them in as consultants, early users, beta testers, advisors Prioritize their needs
- 37. The industry isprimed for change
- 39. Let’s create avirtuous cycle! Software that embeds diversity Diverse, Inclusive People and Communities create empowers
- 40. Diversity-friendly software Shireen Mitchell- @digitalsista Jon Pincus - @jdp23 SXSW 2017 References: http://bit.ly/div5y-links
Editor's Notes
- #3 There’s lots of very valuable and important work going on to make participation more diverse (the first bullet). In this presentation, we’re focusing on the last two bullets.
- #5 There’s lots of very valuable and important work going on to make participation more diverse (the first bullet). In this presentation, we’re focusing on the last two bullets.
- #6 Given industry demographics: able-bodied, cis, straight, white and Asian, guys There are techniques for making software that embraces differences - but they are not yet widely adopted
- #7 Digitall redlining after Trump: Real names and fake news on Facebook https://medium.com/@tressiemcphd/digital-redlining-after-trump-real-names-fake-news-on-facebook-af63bf00bf9e#.k9g7ema70 Fake news software issue: algorithms 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” “If you are a black woman, like me, that can mean my ability to promote my new book (that was advertising), communicate with my college students, share legitimate information or sources with those who cannot access the academy, and shape the preferences of people similarly marked as “black” and “woman” in Facebook’s affinity algorithms to skew away from class-based assumptions is severely undermined.” [Real names software issue: designing reporting system without threat modeling how it can be abused. Real names policy issue: burden falls disproportionately on marginalized people. See http://geekfeminism.wikia.com/wiki/Who_is_harmed_by_a_%22Real_Names%22_policy%3F for more But we’re covering this on the next slide. Process issue: prioritizing reports of “real name” violations over reports of racism and sexism] Propublica on excluding users by race https://www.propublica.org/article/facebook-lets-advertisers-exclude-users-by-race Software issue: mandatory automated race and gender identification (as opposed to optional self-identification) allows affinity targeting to penalize people identified by the algorithms as “black” or “Latino”
- #8 http://blackyouthproject.com/leslie-macs-facebook-ban-is-the-latest-development-in-racially-biased-censorship/ software issue: designing reporting system without threat modeling how it can be abused.
- #9 Software issues: - functionality favors harassers - lack of threat modeling, unintended consequences Also http://nymag.com/thecut/2016/08/a-timeline-of-leslie-joness-horrific-online-abuse.html http://fusion.net/story/327103/leslie-jones-twitter-racism/
- #10 Software issues: - functionality favors harassers - lack of threat modeling, unintended consequences - attempts to improve the situation haven’t worked. Software process issue: not working with the people who are being affected Process issues: Waited till after the election to enforce ToS. Gave voice to alt-righters, while allowing silencing women of color (especially black women). (here I have the data and the WoC who were hacked while they were engaging folks to get out the vote. Twitter did not respond or fix their accounts while those that broke ToS weren’t removed until after the election. https://www.buzzfeed.com/charliewarzel/a-honeypot-for-assholes-inside-twitters-10-year-failure-to-s?utm_term=.ohyn7BZyz#.idBwk0BKo
- #11 company culture of sexism and acceptable sexual misconduct overflowed into their product. Drivers were also sexually assaulting their customer base with little to no actions from Uber. It was clear this was an internal company practice so it was implemented in the software.
- #12 http://www.huffingtonpost.com/eric-holder/airbnbs-work-to-fight-bias-and-discrimination_b_11910438.html https://www.technologyreview.com/s/602355/airbnb-isnt-really-confronting-its-racism-problem/ Ben Edelman’s “Preventing Discrimination at Airbnb” http://www.benedelman.org/news/062316-1.html The solution: Limit the distribution of irrelevant information that facilitates discrimination - hide names and photos. “Names and photos typically indicate the races of Airbnb guests and hosts. But names and photos are not necessary for guests and hosts to do business. Hosts and guests can amply assess one anothers' trustworthiness using the significant other information Airbnb already collects and presents. For these reasons, I contend that the Airbnb site should not reveal sensitive race information until a transaction is confirmed. If guests and hosts don't see names and photos in advance, they simply won't be able to discriminate on that basis.” Allow testing
- #13 https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Software issue: algorithmic bias
- #14 Examples: Twitter not providing tools for people to protect themselves against harassment and abuse Lack of accessibility support in software means that a lot of people can’t even use it Sites that limit gender to male/female, or male/female/other Google photos tagging black people as gorillas: http://www.usatoday.com/story/tech/2015/07/01/google-apologizes-after-photos-identify-black-people-as-gorillas/29567465/ Google’s autocomplete for “women should” … stay at home, be slaves, be in the kitchen http://dhpoco.org/blog/2013/11/19/googles-autocompletion-algorithms-stereotypes-and-accountability/ Google “three black teenagers” as opposed to “three white teenagers” http://www.usatoday.com/story/tech/news/2016/06/09/google-image-search-three-black-teenagers-three-white-teenagers/85648838/ Racial biases in software used to predict future criminal behavior - used in sentencing https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
- #18 Dreamwidth and Django diversity statements
- #26 Work to date focuses on males and females, problem-solving software
- #28 Work to date focuses on males and females, problem-solving software
- #29 AKA “what Twitter doesn’t do”
- #38 Software that truly embraces differences works better for everybody Despite the tech industry’s huge diversity problems, there are a lot of talented women, blacks, Latinxs, Native Americans, QUILTBAGs, seniors, people with disabilities … and we want software that solves our own problems!