From maximizing the crave-ability of food additives to notification addiction, Product Managers have a profound impact on society. In the not too distant future, a number of those Product decisions will be delivered by artificial intelligence. In this talk, we discussed ethical lessons from the history of Product Management and how we can learn from them to build ethical AI. Former Director of Product at Yammer also talked about how to understand data quality, biases, and potential impacts, and learn what your self-driving car will do when it encounters The Trolley Problem.

1. Product Management Ethics in A.I. with
Yammer’s former Director of Product
www.productschool.com

2. FREE INVITE
Join 12,000+ Product Managers on
productschool.com/slack-community

3. Product
Management
2-month part-time Courses

4. Coding
for Managers
2-month part-time Courses

5. Data Analytics
for Managers
2-month part-time Courses

6. Include @productschool and #prodmgmt at the end of your
tweet
Tweet to get a free ticket
for our next Event!

7. Drew Dillon
Tonight’s Speaker

8. Product Management Ethics in A.I.

9. Drew Dillon
Exec In Residence, Costanoa Ventures
@drewdil
https://medium.com/product-man/

14. Ethics

15. Ethics in AI

22. Dr. Julie Manella

30. AI Ethics

31. Observe - actively absorb the entire situation
Orient - understand blind spots and biases
Decide - form a hypothesis for action
Act - test your hypothesis

32. O O D A
People Powered

33. O O D A
Big Data Driven

34. O O D A
Machine Learning

35. O O D A
Artificial Intelligence

37. Interface Isolation

48. Don’t Let Interfaces Isolate
Interfaces are increasingly dynamic
Interface decisions are increasingly made by
machines
We must design systems with a north star beyond
personal preference and metric optimization

49. BIASED
DATA

50. All data has an opinion. You
can't trust the opinions of data
you didn't collect.
Otis Anderson
Data Analyst, OpenDoor

55. What’s wrong with this?

64. Dr. Hermann Joseph Muller

65. Muller began to realize that positive eugenics was
achievable only in a society that had already achieved
radical equality. Eugenics could not be the prelude to
equality. Instead, equality had to be the precondition
for eugenics. Without equality, eugenics would
inevitably falter on the false premise that social ills,
such as vagrancy, pauperism, deviance, alcoholism, and
feeblemindedness were genetic ills—while, in fact, they
merely reflected inequality.

66. “Equality had to be the precondition for eugenics.”

67. “Equality had to be the precondition for eugenics.”has Artificial
Intelligence
To avoid interface isolation.

68. Black Swans

74. Digital Systems Are (mostly) Deterministic

75. Uhh…
Natural Systems Are Stochastic

79. 26 Heads
30 Tails

85. Make Allowances For Black Swans
Attempted optimization of Black Swan events makes
them worse
We barely understand probability, often basing
decisions on small samples
We often are just optimizing the wrong things

86. Feedback & Experimentation

93. Program Feedback & Experimentation
Optimizing systems need probabilistic models &
failure states that incorporate this
Product and Systems Thinking can fix this, but the
whole of our profession needs to level up

94. Transparency

97. Algorithm
Inventors
PHDs
Undergrads
Specializing In AI
Any Engineer
Today

100. Engineers increasingly rely on defaults
Defaults aren’t transparent
∴ We’re heading towards opaque decisions

101. Responsibility
(Liability)

102. “Why do I care about any of
this?”

105. “No one is going to buy a car that kills them.”

106. If Facebook lets users segregate themselves into
smaller and smaller groups leading to isolation and
conflict, whose fault is it?

107. If an autonomous drone bombs a
hospital?

108. If a teenager is repeatedly arrested for typical
boneheaded teenager stuff and they spend the
rest of their lives in and out of the legal system?

111. Deep
Remorse
We Did
Nothing
Wrong
I Was
Paid To
Do A Job

113. Responsibility and ethics are murky and personal
Liability won’t be, but it’ll take a while

114. Okay, Let’s Wrap Up

117. But If We’re Not Thoughtful…
Greater isolation and unequal treatment
Biased data becoming a self-fulfilling prophecy
Leaning into and intensifying tragic disasters
Systems that malfunction without human input
The inability to even understand the malfunction

119. Don’t let interfaces isolate
Understand biases in data
Make Allowances Black Swans
Program Feedback & Experimentation
Encourage Transparency

120. Questions?

121. Part-time Product Management Courses in
San Francisco, Silicon Valley, Los Angeles
and New York
www.productschool.com