Talk delivered 2019-06-25 as part of the Summer Institute in Computational Social Science, held at Princeton University https://compsocialscience.github.io/summer-institute/2019/ Video: https://www.youtube.com/watch?v=0suLWheVji0 title: what should future statisticians, CEO, and senators know about the history and ethics of data? abstract: What should our future statisticians, senators, and CEOs know about the history and ethics of data? How might understanding that history provide tools and resources to future citizens navigating a future shaped by data empowered algorithms? I'll present content from a class co-developed over the past several years with Professor Matt Jones of Columbia's Department of History, based on material absent from both the curriculum for future technologists as well as for future humanists. The intellectual arc traces from the 18th century to present day, beginning with examples of contemporary technological advances, disquieting ethical debates, and financial success powered by panoptic persuasion architectures.

1. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
chris.wiggins@columbia.edu
& &
chris.wiggins@nytimes.com
chris.wiggins@hackNY.org
@chrishwiggins
data-ppf.github.io

2. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
chris.wiggins@columbia.edu
& &
chris.wiggins@nytimes.com
chris.wiggins@hackNY.org
@chrishwiggins
data-ppf.github.io

3. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
chris.wiggins@columbia.edu
& &
joint work with:
Matt Jones
Department of History, Columbia
@nescioquid
data-ppf.github.io

4. what should future
statisticians CEOs, and senators
know about the history and ethics of data?

5. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
1. why history?

6. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
1. why history?
2. why ethics?

7. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
1. why history?
2. why ethics?
3. what we taught

8. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
1. why history?
2. why ethics?
3. what we taught
4. what we learned

9. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
0. preamble: class origin story
1. why history?
2. why ethics?
3. what we taught
4. what we learned

10. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
0. preamble: class origin story
1. why history?
2. why ethics?
3. what we taught
4. what we learned

11. 0. preamble: class origin story

12. 0. preamble: class origin story
$?

13. 0. preamble: class origin story
(we got the grant, btw)

14. 0. preamble: class origin story

15. 0. preamble: class origin story

16. 0. preamble: class origin story
1. why history?
2. why ethics?
3. what we taught
4. what we learned
what should future
statisticians CEOs, and senators
know about the history and ethics of data?

17. 1. why history?

18. 1. why history?

19. 1. why history?
the onion dot com

20. 1. why history?
"Quantum Mechanics", P J E Peebles, 1992

21. 1. why history?
"Quantum Mechanics", P J E Peebles, 1992

22. 1. why history?
"Quantum Mechanics", P J E Peebles, 1992

23. 1. why history?
"Quantum Mechanics", P J E Peebles, 1992
truth is contested

24. 1. why history?

25. 1. why history?

26. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
0. preamble: class origin story
1. why history?
2. why ethics?
3. what we taught
4. what we learned

27. 1. why ethics?

28. 1. why ethics?
something is wrong on the internet

29. 1. why ethics?
2017-09-05: cathy o’neil
something is wrong on the internet

30. 1. why ethics?
2017-09-05: cathy o’neil
2018-01-08: safiya noble
something is wrong on the internet

31. 1. why ethics?
2017-09-05: cathy o’neil
2018-01-08: safiya noble
2018-01-23: virginia eubanks
something is wrong on the internet

32. 1. why ethics?
2017-09-05: cathy o’neil
2018-01-08: safiya noble
2018-01-23: virginia eubanks
2019-01-15:
shoshana zuboff
(among increasingly many others)
something is wrong on the internet

33. 1. why ethics?
1. fuzzies
2. techies

34. 1. why ethics?
1. fuzzies
2. techies

35. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
0. preamble: class origin story
1. why history?
2. why ethics?
3. what we taught
4. what we learned

37. (really this is just weeks 3-11)

38. (really this is just weeks 3-11)

39. (really this is just weeks 3-11)

40. (really this is just weeks 3-11)

45. close each week with:

46. close each week with:
- how did new capabilities rearrange power?
(who can now do what, from what, to whom?)

47. close each week with:
- how did new capabilities rearrange power?
(who can now do what, from what, to whom?)
- role of

48. close each week with:
- how did new capabilities rearrange power?
(who can now do what, from what, to whom?)
- role of
1. rights

49. close each week with:
- how did new capabilities rearrange power?
(who can now do what, from what, to whom?)
- role of
1. rights
2. harms

50. close each week with:
- how did new capabilities rearrange power?
(who can now do what, from what, to whom?)
- role of
1. rights
2. harms
3. justice

51. close each week with:
- how did new capabilities rearrange power?
(who can now do what, from what, to whom?)
- role of
1. rights
2. harms
3. justice
(week 1 & 2 had plenty of harms+injustice)

52. github.com/data-ppf/data-ppf.github.io/wiki/Syllabus
3. what we taught: 14 weeks: Tuesday discussion

53. 1 intro
2 setting the stakes
3 risk and social physics
4 statecraft and quantitative racism
5 intelligence, causality, and policy
6 data gets real: mathematical baptism
7 WWII, dawn of digital computation
8 birth and death of AI
9 big data, old school (1958-1980)
10 data science, 1962-2017
11 AI2.0
12 ethics
13 present problems & VC-backed attention economy
14 future solutions
github.com/data-ppf/data-ppf.github.io/wiki/Syllabus
3. what we taught: 14 weeks: Tuesday discussion

54. 3. what we taught: 14 weeks: Thursday Labs
github.com/data-ppf/data-ppf.github.io/wiki/Syllabus

55. 1. first steps in Python interrogating the UCI dataset
2. EDA with the UCI dataset
3. Quetelet and GPAs
4. Galton
5. statistics and society; Yule, Spearman, Simpson
6. p-hacking; Fisher
7. the first data science
8. AI 1.0; Expert systems; Perceptron
9. databases and recsys; the Netflix Prize story
10. trees along with in-lab lecture on trees
11. interactive: 3 ML’s; FAT 1.0 disparate impact, disparate
treatment, and COMPAS
12. normative+technical approaches to defining and defending
privacy; our own database of ruin: constructing and de-
identifying; FAT 2.0 featuring ToS/EULAs
13. problems along with in-lab lecture on NSA history
14. solutions
3. what we taught: 14 weeks: Thursday Labs
github.com/data-ppf/data-ppf.github.io/wiki/Syllabus

56. 1 intro
2 setting the stakes
3 risk and social physics
4 statecraft and quantitative racism
5 intelligence, causality, and policy
6 data gets real: mathematical baptism
7 WWII, dawn of digital computation
8 birth and death of AI
9 big data, old school (1958-1980)
10 data science, 1962-2017
11 AI2.0
12 ethics
13 present problems & VC-backed attention economy
14 future solutions
2. why ethics?
3. what we taught: 14 weeks: Tuesdays
4. what we learned

57. e.g., week 4 regression & quantitative racism

58. e.g., week 4 regression & quantitative racism

59. e.g., week 4 regression & quantitative racism

60. e.g., week 4 regression & quantitative racism

61. e.g., week 4 regression & quantitative racism

62. e.g., week 4 regression & quantitative racism

63. e.g., week 4 regression & quantitative racism

64. e.g., week 4 regression & quantitative racism

65. e.g., week 4 regression & quantitative racism

66. e.g., week 5 IQ, policy and causality

67. e.g., week 5 IQ, policy and causality

68. e.g., week 5 IQ, policy and causality

69. e.g., week 5 IQ, policy and causality

70. e.g., week 5 IQ, policy and causality

71. e.g., week 5 IQ, policy and causality

72. e.g., week 5 IQ, policy and causality

73. e.g., week 5 IQ, policy and causality

74. e.g., week 5 IQ, policy and causality

75. e.g., week 7 “women at the dawn…” (Abbate)

76. e.g., week 7 “women at the dawn…” (Abbate)

77. e.g., week 7 “women at the dawn…” (Abbate)

78. e.g., week 7 “women at the dawn…” (Abbate)

79. e.g., week 7 “women at the dawn…” (Abbate)

80. e.g., week 7 “women at the dawn…” (Abbate)

81. e.g., week 7 “women at the dawn…” (Abbate)

82. e.g., week 7 “women at the dawn…” (Abbate)

83. e.g., week 7 “women at the dawn…” (Abbate)

84. e.g., week 9 “big data” + privacy 1950-1980

85. e.g., week 9 “big data” + privacy 1950-1980

86. e.g., week 9 “big data” + privacy 1950-1980

87. e.g., week 9 “big data” + privacy 1950-1980

88. e.g., week 9 “big data” + privacy 1950-1980

89. e.g., week 9 “big data” + privacy 1950-1980

90. e.g., week 9 “big data” + privacy 1950-1980
- 1967: FOIA
- 1970: Social Security Number Task Force
- 1970: Fair Credit Reporting Act
- 1973: Watergate hearings
- 1974: Privacy Act
- 1975: "Church" (Select Committee to Study
Governmental Operations with Respect to Intelligence
Activities of the United States Senate)
- 1975: Rockefeller Commission
- 1975: Pike Committee
- 1974: The Family Educational Rights and Privacy Act

91. e.g., week 9 “big data” + privacy 1950-1980

92. e.g., week 9 “big data” + privacy 1950-1980

93. e.g., week 9 “big data” + privacy 1950-1980

94. e.g., week 9 “big data” + privacy 1950-1980

95. e.g., week 10 “data science”, 1962-present

96. e.g., week 10 “data science”, 1962-present

97. e.g., week 10 “data science”, 1962-present

98. e.g., week 10 “data science”, 1962-present

99. e.g., week 10 “data science”, 1962-present

100. e.g., week 10 “data science”, 1962-present

101. “three kinds
of ML”
(interactive)

102. “three kinds
of ML”
(interactive)

103. “three kinds
of ML”
(interactive)

104. “three kinds
of ML”
(interactive)

105. “three kinds
of ML”
(interactive)

106. e.g., week 12 the ethics of data

107. e.g., week 12 the ethics of data

108. e.g., week 12 the ethics of data

109. e.g., week 12 the ethics of data

110. e.g., week 12 the ethics of data

111. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont

112. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1. articulate principles
2. articulate tensions among them
3. design to support them
- interaction design
- process design
- (in this case, the IRB process)

113. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont

114. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont

115. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont

116. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1. articulate ethics as principles
2. articulate tensions among them
3. articulate design to support them
5.

117. what we talk about when we talk about ethics

118. what we talk about when we talk about ethics
“ethics”

119. what we talk about when we talk about ethics
“ethics”
philosophy

120. what we talk about when we talk about ethics
“ethics”
philosophy
logic

121. what we talk about when we talk about ethics
“ethics”
philosophy
logic
math

122. what we talk about when we talk about ethics
“ethics”
philosophy
logic
math
proof

123. what we talk about when we talk about ethics
“ethics”
:)
philosophy
logic
math
proof

124. what we talk about when we talk about ethics
“ethics”
philosophy
logic

125. what we talk about when we talk about ethics
“ethics”
philosophy
logic
CS

126. what we talk about when we talk about ethics
“ethics”
philosophy
logic
CS
APIs

127. what we talk about when we talk about ethics
“ethics”
:)
philosophy
logic
CS
APIs

128. what we talk about when we talk about ethics
“ethics”
philosophy

129. what we talk about when we talk about ethics
“ethics”
philosophy sociology

130. what we talk about when we talk about ethics
“ethics”
philosophy sociology
PR/
“ethics
theater” -MW

131. what we talk about when we talk about ethics
“ethics”
philosophy sociology
PR/
“ethics
theater” -MW
:(

132. “ethics”
philosophy sociology
(define) (design)

133. “ethics”
philosophy sociology
(define) (design)

134. e.g., week 12 the ethics of data
Belmont principles

135. e.g., week 12 the ethics of data
Belmont principles
1. respect for personhood

136. e.g., week 12 the ethics of data
Belmont principles
1. respect for personhood
- informed consent -> autonomy

137. e.g., week 12 the ethics of data
Belmont principles
1. respect for personhood
- informed consent -> autonomy
2. beneficence

138. e.g., week 12 the ethics of data
Belmont principles
1. respect for personhood
- informed consent -> autonomy
2. beneficence
- do no harm -> balance risk+benefit

139. e.g., week 12 the ethics of data
Belmont principles
1. respect for personhood
- informed consent -> autonomy
2. beneficence
- do no harm -> balance risk+benefit
3. justice

140. e.g., week 12 the ethics of data
Belmont principles
1. respect for personhood
- informed consent -> autonomy
2. beneficence
- do no harm -> balance risk+benefit
3. justice
- legal-> fair, e.g., “veil of ignorance”

141. e.g., week 12 the ethics of data
Belmont principles
1. respect for personhood
- informed consent -> autonomy
2. beneficence
- do no harm -> balance risk+benefit
3. justice
- legal-> fair, e.g., “veil of ignorance”

142. e.g., week 12 the ethics of data
Belmont principles
1. respect for personhood
- informed consent -> autonomy
2. beneficence
- do no harm -> balance risk+benefit
3. justice
- legal-> fair, e.g., “veil of ignorance”
gives analytical, hierarchical, durable framework
for ethical audit of decisions,
from which rules, code, “design” should derive

143. e.g., week 12 the ethics of data
from: Wagner, Ben. "Ethics as an Escape from Regulation:
From ethics-washing to ethics-shopping?." (2018).

144. e.g., week 12 the ethics of data
1. “External Participation: early and regular engagement with
all relevant stakeholders.
from: Wagner, Ben. "Ethics as an Escape from Regulation:
From ethics-washing to ethics-shopping?." (2018).

145. e.g., week 12 the ethics of data
1. “External Participation: early and regular engagement with
all relevant stakeholders.
2. Provide a mechanism for external independent oversight.
from: Wagner, Ben. "Ethics as an Escape from Regulation:
From ethics-washing to ethics-shopping?." (2018).

146. e.g., week 12 the ethics of data
1. “External Participation: early and regular engagement with
all relevant stakeholders.
2. Provide a mechanism for external independent oversight.
3. Ensure transparent decision-making procedures on why
decisions were taken.
from: Wagner, Ben. "Ethics as an Escape from Regulation:
From ethics-washing to ethics-shopping?." (2018).

147. e.g., week 12 the ethics of data
1. “External Participation: early and regular engagement with
all relevant stakeholders.
2. Provide a mechanism for external independent oversight.
3. Ensure transparent decision-making procedures on why
decisions were taken.
4. Develop a stable list of non-arbitrary of standards where
the selection of certain values, ethics and rights over
others can be plausibly justified.
from: Wagner, Ben. "Ethics as an Escape from Regulation:
From ethics-washing to ethics-shopping?." (2018).

148. e.g., week 12 the ethics of data
1. “External Participation: early and regular engagement with
all relevant stakeholders.
2. Provide a mechanism for external independent oversight.
3. Ensure transparent decision-making procedures on why
decisions were taken.
4. Develop a stable list of non-arbitrary of standards where
the selection of certain values, ethics and rights over
others can be plausibly justified.
5. Ensure that ethics do not substitute fundamental rights or
human rights.
from: Wagner, Ben. "Ethics as an Escape from Regulation:
From ethics-washing to ethics-shopping?." (2018).

149. e.g., week 12 the ethics of data
1. “External Participation: early and regular engagement with
all relevant stakeholders.
2. Provide a mechanism for external independent oversight.
3. Ensure transparent decision-making procedures on why
decisions were taken.
4. Develop a stable list of non-arbitrary of standards where
the selection of certain values, ethics and rights over
others can be plausibly justified.
5. Ensure that ethics do not substitute fundamental rights or
human rights.
6. Provide a clear statement on the relationship between the
commitments made and existing legal or regulatory
frameworks, in particular on what happens when the two are
in conflict.”
from: Wagner, Ben. "Ethics as an Escape from Regulation:
From ethics-washing to ethics-shopping?." (2018).

150. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont

151. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1.articulate ethics as principles

152. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1.articulate ethics as principles
- consistent w/norms, rights, philosophy

153. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1.articulate ethics as principles
- consistent w/norms, rights, philosophy
2.articulate tensions among them

154. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1.articulate ethics as principles
- consistent w/norms, rights, philosophy
2.articulate tensions among them
- e.g., “ends” vs “means”

155. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1.articulate ethics as principles
- consistent w/norms, rights, philosophy
2.articulate tensions among them
- e.g., “ends” vs “means”
3.articulate design to support them

156. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1.articulate ethics as principles
- consistent w/norms, rights, philosophy
2.articulate tensions among them
- e.g., “ends” vs “means”
3.articulate design to support them
- interaction design

157. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1.articulate ethics as principles
- consistent w/norms, rights, philosophy
2.articulate tensions among them
- e.g., “ends” vs “means”
3.articulate design to support them
- interaction design
- process design

158. e.g., week 12 the ethics of data
history: Tuskegee -> Belmont
1.articulate ethics as principles
- consistent w/norms, rights, philosophy
2.articulate tensions among them
- e.g., “ends” vs “means”
3.articulate design to support them
- interaction design
- process design
- (in this case, the IRB process)

159. e.g., week 12 define + design for ethics
“The Commission’s deliberations on Institutional Review Boards began
with the premise that investigators should not have sole
responsibility for determining whether research involving human
subjects fulfills ethical standards. Others who are independent of
the research must share this responsibility, because investigators
have a potential conflict by virtue of their concern with the pursuit
of knowledge as well as the welfare of the human subjects of their
research.”
1978-09-01 IRB recommendation

160. e.g., week 12 define + design for ethics
reminder: “design is the intentional
solution to a problem within a set of
constraints.” — Mike Monteiro
“The Commission’s deliberations on Institutional Review Boards began
with the premise that investigators should not have sole
responsibility for determining whether research involving human
subjects fulfills ethical standards. Others who are independent of
the research must share this responsibility, because investigators
have a potential conflict by virtue of their concern with the pursuit
of knowledge as well as the welfare of the human subjects of their
research.”
1978-09-01 IRB recommendation

161. e.g., week 12 define + design for ethics
reminder: “design is the intentional
solution to a problem within a set of
constraints.” — Mike Monteiro
“The Commission’s deliberations on Institutional Review Boards began
with the premise that investigators should not have sole
responsibility for determining whether research involving human
subjects fulfills ethical standards. Others who are independent of
the research must share this responsibility, because investigators
have a potential conflict by virtue of their concern with the pursuit
of knowledge as well as the welfare of the human subjects of their
research.”
1978-09-01 IRB recommendation

162. e.g., week 12 ethics lab:
- database of ruin
- k-anonymity
- terms of service
kdnuggets.com (2014):
“Big Data Comic Explains the Current State of Privacy”

163. e.g., week 13 (present) problems

164. e.g., week 13 (present) problems

165. e.g., week 13 (present) problems

166. e.g., week 13 (present) problems

167. e.g., week 13 (present) problems

168. e.g., week 13 (present) problems

169. e.g., week 14 (future) solutions

170. e.g., week 14 (future) solutions

171. e.g., week 14 (future) solutions
3-player unstable game (adapted from Janeway)

172. e.g., week 14 (future) solutions
3-player unstable game (adapted from Janeway)

173. e.g., week 14 (future) solutions
3-player unstable game (adapted from Janeway)

174. e.g., week 14 (future) solutions
3-player unstable game (adapted from Janeway)

175. e.g., week 14 (future) solutions
3-player unstable game (adapted from Janeway)

176. e.g., week 14 (future) solutions
3-player unstable game (adapted from Janeway)

177. e.g., week 14 (future) solutions
3-player unstable game (adapted from Janeway)

178. e.g., week 14 (future) solutions
3-player unstable game (adapted from Janeway)

179. e.g., week 14 (future) solutions
3-player unstable game (adapted from Janeway)

180. e.g., week 14 (future) solutions
3-player unstable game (adapted from Janeway)

181. e.g., week 14 (future) solutions
3-player unstable game (adapted from Janeway)

182. e.g., example: IRB
3-player unstable game (adapted from Janeway)

183. e.g., week 14 (future) solutions
2017-10-15 (FT) “privacy has become a competitive
advantage.”
2015-10-01, APPL: “privacy is a fundamental human right”
2019-04-28 FB: “The future is private”
2019-02-07 CSCO: “privacy is a fundamental human right”

184. e.g., week 14 (future) solutions

185. e.g., week 14 (future) solutions

186. e.g., week 14 (future) solutions

187. e.g., week 14 (future) solutions
- GDPR
- California Consumer Privacy Act (CCPA)
- rise of “Hipster Antitrust”
- CDA 230
- FTC “Do Not Track Me Online Act of 2011”
- FEC “Honest Ads Act”
- “SEC for the technology industry” - DiResta
- …

188. e.g., week 14 (future) solutions

189. e.g., week 14 (future) solutions

190. e.g., week 14 (future) solutions

191. e.g., week 14 (future) solutions

192. e.g., week 14 (future) solutions

193. e.g., week 14 (future) solutions

194. 0. preamble: class origin story
1. why history?
2. why ethics?
3. what we taught
4. what we learned
what should future
statisticians CEOs, and senators
know about the history and ethics of data?

195. 4. what we learned
1. history + ethics:
how to integrate throughout a “tech” education
2. draw parallels to today
3. capabilities rearrange power
4. story of “data” is story of truth+power
- contested
5. find the future by analyzing
- present contests
- present powers

196. what should future
statisticians CEOs, and senators
know about the history and ethics of data?
data-ppf.github.io
“ethics”
define & design
chris.wiggins@columbia.edu
@chrishwiggins