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Flupa UX Days 2018 | Sara Wachter-Boettcher (EN)

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DESIGNING INCLUSIVE PRODUCTS
Conférence de Sara Wachter-Boettcher

Même si nos projets commencent toujours avec de très bonnes intentions, on se retrouve souvent avec des produits pilotés par des stéréotypes toxiques qui sont véhiculés dans la culture tech : des balances connectées qui déduisent que tout le monde veut perdre du poids, des formulaires qui ne correspondent pas aux personnes transgenres, des bots lecteurs de curriculum qui désavantagent les femmes,les systèmes de reconnaissance faciale qui n’arrivent pas à identifier les personnes de couleur…
Aujourd’hui, la technologie a pris une place centrale dans le quotidien de nos utilisateurs jusqu’à aller dans le moindre recoin de leur intimité, nous avons notre part de responsabilité par nos décisions lorsque ces produits incluent ou rejettent des utilisateurs. Durant cette keynote, Sara Wachter-Boettcher expliquera comment la culture de l’industrie tech est en train de créer des produits qui exploitent des préjugés, manipulent et blessent des utilisateurs, mais nuisent également à la démocratie. Elle nous présentera également le comportement à adopter pour éviter de créer ce type de produit : comment concevoir des hypothèses dans notre processus de travail, vérifier que les décisions prises autour du produit soient correctes auprès de différents utilisateurs et différentes situations, avoir des discussions avec ses équipes et les entreprises et poursuivre une démarche de conception plus éthique et inclusive pour notre industrie.

Plus d'infos sur : uxday.flupa.eu/designing-inclusive-products

Published in: Design
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Flupa UX Days 2018 | Sara Wachter-Boettcher (EN)

  1. 1. DESIGNING INCLUSIVE PRODUCTS Sara Wachter-Boettcher // June 2018
  2. 2. Image: Pew Research
  3. 3. ‘‘One Spanish-speaking respondent said she was uncomfortable “registering” other household members… when she realized she would have to provide information on others who live with her. She mentioned being afraid because of the current political climate and news reports about changing immigration policy. —Center for Survey Measurement 
 “Pretesting” usability study report, 2017
  4. 4. ‘‘A second Spanish-speaking respondent filled out information about herself and three family members but intentionally left three or four roomers off the roster because, “This frightens me, given how the situation is now” and mentioned being worried because of their “[immigration] status.” —Center for Survey Measurement 
 “Pretesting” usability study report, 2017
  5. 5. • How many members of Congress does your state get? • Where are voting district boundaries? • How does infrastructure like roads and bridges get planned? • Where do we build schools and libraries?
  6. 6. Census data is private…
  7. 7. Census data is private… Except when it isn’t.
  8. 8. National Archives
  9. 9. Every design decision has consequences.
  10. 10. flickr.com/photos/hjjanisch/8606288865/ (CC BY-ND 2.0)
  11. 11. 8:00 p.m.9:03 p.m.
  12. 12. To recap: • There’s no way to turn it off • This is dangerous for people with eating disorders • This feels shamey • “Average” calorie counts are wildly inaccurate • Not all calories are equal • A cupcake is not a useful metric • Pink cupcakes are not neutral—they have social and culture encoding (feminine, white, middle class) • This perpetuates diet culture
  13. 13. It took one hour to document these flaws.
  14. 14. It took three hours to shut the feature down.
  15. 15. How many hours did it take to build?
  16. 16. flickr.com/photos/3059349393/8000156483/ (CC BY 2.0)
  17. 17. ‘‘Silicon Valley is run by people [who] want to be in the tech business, but are in the people business. They are way, way in over their heads. —Zeynep Tufekci
  18. 18. ‘‘Twitter is failing in its responsibility to respect women’s rights online by inadequately investigating and responding to reports of violence and abuse in a transparent manner. —Amnesty International, 
 March 2018
  19. 19. ‘‘We love instant, public, global messaging… But we didn’t fully predict or understand the real-world negative consequences. We acknowledge that now… 
 We aren’t proud of how people have taken advantage of our service, or our inability to address it fast enough. —Jack Dorsey, March 2018
  20. 20. ‘‘We suck at dealing with abuse and trolls on the platform and we’ve sucked at it for years… We lose core user after core user by not addressing simple trolling issues that they face every day. I’m frankly ashamed of how poorly we’ve dealt with this issue during my tenure as CEO. It’s absurd. There’s no excuse for it. —Dick Costolo, February 2015
  21. 21. Screensh0t from a knockoff Peppa Pig video (James Bridle)
  22. 22. • Cartoons about torture and suicide • Sexualized characters and themes • Violence and weapons • Kids being tied up and hurt • Kids vomiting and writhing in pain
  23. 23. Image: Google I/O
  24. 24. Image: Google I/O • How will we trust who we’re talking to? • Is it OK for a bot to pretend it’s human? • What does recording the call mean for privacy? • Is it fair to outsource task to bots and expect low-wage workers to deal with it?
  25. 25. This isn’t just about moderation. This is about product design.
  26. 26. WISHFUL THINKING
  27. 27. The more you talk, the smarter Tay gets.
  28. 28. ‘‘We stress-tested Tay under a variety of conditions, specifically to make interacting with Tay a positive experience. —Peter Lee, Microsoft Research
  29. 29. ‘‘We stress-tested Tay under a variety of conditions, specifically to make interacting with Tay a positive experience. —Peter Lee, Microsoft Research
  30. 30. From Dan Hon
  31. 31. From Dan Hon
  32. 32. From Eric Meyer
  33. 33. From @oliviasolon
  34. 34. From @sallyrooney
  35. 35. ‘‘We talked about getting rid of it but it performs kinda great :/ —Tag Savage, lead writer at Tumblr
  36. 36. Inclusion may be inconvenient to your business model.
  37. 37. BIASED NORMS
  38. 38. ‘‘We are deeply sorry for this unquestionably serious issue. It is an unfortunate side-effect of the underlying neural network caused by the training set bias, not intended behaviour. — Yaroslav Goncharov, FaceApp CEO 
  39. 39. ‘‘We are deeply sorry for this unquestionably serious issue. It is an unfortunate side-effect of the underlying neural network caused by the training set bias, not intended behaviour. — Yaroslav Goncharov, FaceApp CEO 
  40. 40. From Jacky Alciné
  41. 41. From Jacky Alciné
  42. 42. ‘‘We’re also working on longer-term fixes around both linguistics (words to be careful about in photos of people) … and image recognition itself (e.g., better recognition of dark-skinned faces). —Yonatan Zunger, former Googler
  43. 43. ‘‘We’re also working on longer-term fixes around both linguistics (words to be careful about in photos of people) … and image recognition itself (e.g., better recognition of dark-skinned faces). —Yonatan Zunger, former Googler
  44. 44. ‘‘When the person in the photo is a white man, the software is right 99 percent of the time. But the darker the skin, the more errors arise — up to nearly 35 percent for images of darker skinned women. —“Facial Recognition Is Accurate, if You’re a White Guy”
 by Steve Lohr, The New York Times
  45. 45. ‘‘When the person in the photo is a white man, the software is right 99 percent of the time. But the darker the skin, the more errors arise — up to nearly 35 percent for images of darker skinned women. —“Facial Recognition Is Accurate, if You’re a White Guy”
 by Steve Lohr, The New York Times
  46. 46. Courtesy of Kodak and Hermann Zschiegner
  47. 47. These biases get baked into all kinds of products.
  48. 48. flickr.com/photos/147489968@N06/29300385324 (www.paintzen.com - CC BY-ND 2.0) COCO: More than 100,000 images from the web, labeled.
  49. 49. flickr.com/photos/147489968@N06/29300385324 (www.paintzen.com - CC BY-ND 2.0) AI trained on it associates kitchen implements with women.
  50. 50. HOW WE CHANGE THINGS
  51. 51. Practice Process Priorities
  52. 52. Practice Process Priorities
  53. 53. ‘‘If you’re not asking yourself “how could this be used to hurt someone” in your design/engineering process, you’ve failed. — Zoe Quinn
  54. 54. ASSUME
  55. 55. • The user had a good year. • The user wants to relive their year. • The user wants to share their year. • The user’s most popular content is positive.
  56. 56. • Identity • Location • Emotional state • Physical state • Personal history • Lifestyle • Goals • Pain points
  57. 57. ‘‘My hair type is what’s called ‘4C hair,’ given the level of coiliness. I learned that I needed to add that to my searches in order to find things. It shouldn’t be that way. — Candice Morgan, 
 Head of Diversity & Inclusion, Pinterest
  58. 58. Practice Process Priorities
  59. 59. If it’s important, it’s planned.
  60. 60. If it’s important, it’s resourced.
  61. 61. If it’s important, it’s evaluated.
  62. 62. Make inclusive design an explicit part of: • Project roles and responsibilities • Roadmapping and project planning • Use cases and scenarios • Content crits and editing cycles • Project postmortems • Employee evaluations
  63. 63. Practice Process Priorities
  64. 64. ‘‘We’re an idealistic and optimistic company. For the first decade, we really focused on all the good that connecting people brings… But it’s clear now that we didn’t do enough. We didn’t focus enough on preventing abuse and thinking through how people could use these tools to do harm as well. —Mark Zuckerberg, April 2018
  65. 65. ‘‘As most companies grow, they slow down too much because they’re more afraid of making mistakes than they are of losing opportunities by moving too slowly. We have a saying: “Move fast and break things.” The idea is that if you never break anything, you’re probably not moving fast enough. —Mark Zuckerberg, 2012
  66. 66. ‘‘As most companies grow, they slow down too much because they’re more afraid of making mistakes than they are of losing opportunities by moving too slowly. We have a saying: “Move fast and break things.” The idea is that if you never break anything, you’re probably not moving fast enough. —Mark Zuckerberg, 2012
  67. 67. If you never slow down, you probably have no idea what you’re breaking.
  68. 68. Image: Pixabay (CC0)
  69. 69. Image: Josh Mattson (CC - BY NC)
  70. 70. DESIGN TO INCLUDE
  71. 71. Image: Pew Research
  72. 72. our work has
 power flickr.com/photos/vchili/6028132840
  73. 73. how will you 
 use yours? flickr.com/photos/vchili/6028132840
  74. 74. @sara_ann_marie rareunion.com sarawb.com thank you

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