This document discusses ethics in technology and provides recommendations for technology companies to address ethical issues. It notes that software and algorithms reflect biases and can have unintended consequences like marginalization. It recommends that technology companies ask questions about why and whether they should solve problems, involve users in decisions, plan for misuse cases, consider stressful situations, prioritize transparency, and continually learn about ethics issues. The key message is that technology companies need to focus on ethics and consider how their products impact users.

1. Hello!

2. ethics

3. ethics
principles which govern a behavior or the conducting of
an activity, delineating what is acceptable and what isn’t.

4. tech ethics

5. not just “design”

6. our industry needs ethics

7. the scale of our work is massive

8. software isn’t neutral
(sorry)

9. technically correct
=/=
unbiased

10. “we want to solve problems”

11. “we want to solve hard problems”

12. “we make data-driven decisions”

13. CATHY O’NEIL
Algorithms are opinions embedded
in code

14. examples & consequences

15. failure takes many forms
Selling or giving away data
Biased algorithms making discriminatory decisions
Missing or poorly-built features which enable marginalization
or harassment
Design “dark patterns”

16. Siri, Google Now, Cortana, S Voice
No helpline was given for depression
None recognized statements asking for help with abuse or sexual
assault. Siri, Google Now, and S Voice did not recognize the
concern.
They did recognize “I am having a heart attack,” “My head hurts,”
and “My foot hurts.”

17. Facebook

18. TRINE FALBE
The biggest problem with basing decisions on
what you think and feel, or what is easiest from
a technical perspective, is that it doesn’t involve
the people you are serving.

19. NextDoor

21. PureGym

22. Orbitz + Wells Fargo

23. SOURCE

24. casualties

25. identity

28. transparency & trust

29. self-expression

31. autonomy

32. what can you do?

33. GDPR compliance

34. １. Ask questions

35. “Why do I want to solve this
problem?”

36. “Should I solve this problem?”

38. Which kinds of personal information are used?
What do you need as input?
Will the person volunteer it, will you observe it or acquire it?
How will you acquire it?
What data do we need?

39. ２. Build the right teams

40. ３. Hire external consultants

41. TRINE FALBE
“The biggest problem with basing decisions on what you
think and feel, or what is easiest from a technical
perspective, is that it doesn’t involve the people you are
serving.”

42. “THE REGRET TEST” - NIR EYAL
“If people knew everything the product designer knows,
would they still execute the intended behaviour? Are they
likely to regret doing this?”

43. ４. Plan for misuse cases

44. ５. Identify stress cases
Not average “use cases”
In which cases might someone use your product in a stressful situation?
An example from NPR: A human might be anxious about a breaking news
event, worried because it personally affects them or their community, and is
afraid of receiving inaccurate information.

45. LIBBY BAWCOMBE, NPR
Identifying stress cases helps us see the spectrum of varied and
imperfect ways humans encounter our products, especially
taking into consideration moments of stress, anxiety and
urgency. [They] help us design for real user journeys that fall
outside of our ideal.

46. ６. Empathy

47. ６. Humility

48. ７. Measure what matters to users

49. ８. Be transparent!

50. ９. Document your principles

51. １０. Move slowly and build
with purpose.

52. Be the “voracious learner” you claim to be
Take a course in tech ethics: there’s over ２００ of them.
Read a book: Algorithms of Oppression, Technically Wrong, Weapons of Math
Destruction, Automating Inequality, We Are Data
Read the research: SimplySecure, Cracked Labs, Data & Society

53. What you say doesn’t matter

54. You are what you do, not what
you say

55. mistakes are OK
what matters is how you fix them

56. NextDoor

58. Team Clueless presenting their project ‘Earthbound' at #Summerhack２０１８

60. this is a hard problem that demands
the skills we claim to have