Presentation by Aaron Halfaker, PhD, principal research scientist at the Wikimedia Foundation, at Quora ML Workshop: Protecting Online Spaces with Applied Machine Learning, on September 27, 2017.

3. User:ClueBot NG
HotCat
Engineering at the
Intersection of Productive
Efficiency, Ideology, and
Ethical AI in Wikipedia
Wikimedia
Research
Aaron Halfaker
ahalfaker@wikimedia.org

5. Planet
Earth
(6.97 billion)
data from March 2012

6. Planet
Earth
(6.97 billion)
data from March 2012
1:10
Monthly
Wikipedia
Readers
(500 million)

7. Monthly Wikipedia Editors
(113,304)
Planet
Earth
(6.97 billion)
data from March 2012
1:10000
1:10
Monthly
Wikipedia
Readers
(500 million)

8. Monthly Wikipedia Editors
(113,304)
Planet
Earth
(6.97 billion)
data from March 2012
Monthly
Wikipedia
Readers
(500 million)
1:10
1:10000

9. Monthly Wikipedia Editors
(113,304)
Planet
Earth
(6.97 billion)
data from March 2012
Monthly
Wikipedia
Readers
(500 million)
1:10
1:10000

10. [edit]
Monthly Wikipedia Editors
(100,000)
1:10,000
Wikimedia Foundation Staff
(~225)
1:1000
Monthly Wikipedia
Readers (500M)

11. Wikimedia
Research

12. Story Time!

13. 1. Wikipedia as a socio-technical system
○ Systems-thinking & Biological metaphors ~

14. 1. Wikipedia as a socio-technical system
○ Systems-thinking & Biological metaphors
2. Critique of algorithmic quality control
○ Standpoint epistemology
○ Encoding of ideology in technology
~

15. 1. Wikipedia as a socio-technical system
○ Systems-thinking & Biological metaphors
2. Critique of algorithmic quality control
○ Standpoint epistemology
○ Encoding of ideology in technology
3. Infrastructure for socio-technical change
○ “progress catalyst”
○ Hearing to speech vs. Speaking to be heard
○ The dangers of “subjective algorithms”
~

16. Part 1. The
socio-technical of
Wikipedia

17. What is Wikipedia?

18. The world’s largest encyclopedia
~5 million articles
in English

26. A wiki
● Anyone can edit
● Shared authorship
Flipped publication model
● publish first
● review later (maybe)

27. An online community
● ~100k active volunteer editors
WikiProject Video
Games
WikiProject Medicine

28. A system
Available
Human
Attention

29. A system
Available
Human
Attention
Input
Output

30. A system
Available
Human
Attention
Input
Output
Socio-technical

31. Social Technical
https://commons.wikimedia.org/wiki/File:Commodore_P
ET_4016,_Google_NY_office_computer_museum.jpg
https://commons.wikimedia.org/wiki/File:Kadokawa_office_(2).jpg

32. Social Technical
https://commons.wikimedia.org/wiki/File:Marcus_Bai
ns_Line.png https://commons.wikimedia.org/wiki/File:Balsa-gpg.png

33. Socio-technical

35. Technologist

36. Techno-biologist?

37. Bacterium Paramecium

38. Bacterium Paramecium
DNA
ribosomes
vacuole

39. Bacterium Paramecium
DNA
ribosomes
vacuole
vacuole
contractile vacuole
endoplasmic
reticulum
oral groove
macronucleus
micronucleus

40. Bacterium Paramecium

41. Bacterium Paramecium
Dunbar’s number: ~150
Via commons. CC-BY-SA 2.0

42. Bacterium ParameciumBare-amecium Organelles
Robots
& tools
Policies &
Guidelines

43. socio - technical

44. socio-technical

45. ~
System with specialized
sub-systems
System with specialized
sub-systems

47. Work allocation
● Identify, prioritize and assign tasks

48. Work allocation
● Largely for free due to Linus’ Law

49. Linus’s Law?

50. Linus’s Law?
Eric
Raymond

51. Linus’s Law?
“given enough eyeballs, all bugs are shallow”

52. Linus’s Law?
“given enough eyeballs, all bugs are shallow”
yessss…
bugssss….

53. Linus’s Law?
“given enough eyeballs, all bugs are shallow”

54. Linus’s Law?
“given enough eyeballs, all bugs are shallow”
!

55. Linus’s Law?
visibility is critical to open collaboration

56. A corollary for Wikipedia
Given that enough people see an incomplete
article, all potential contributions to that article
will be easy for someone.
!
Works in theory -- Stvilia, B., et al. (2008). Information quality work organization in
Wikipedia. JASIST, 59(6), 983-1001.
Part of becoming a Wikipedian -- Bryant, S. L., Forte, A., & Bruckman, A. (2005,
November). Becoming Wikipedian. GROUP (pp. 1-10). ACM.
We can support visibility with technology -- Cosley, D., et al. (2007, January).
SuggestBot: using intelligent task routing to help people find work in wikipedia. IUI
(pp. 32-41). ACM.
Bad things happen when we take it away -- Schneider, J., et al. (2014, August).
Accept, decline, postpone. OpenSym(p. 26). ACM.

57. Work allocation
● Identify, prioritize and assign tasks
Regulation of behavior
● Norm formation, propagation and enforcement

60. Prescriptive
Idea for a Norm
Descriptive
Observation of
consistent behavior

61. Prescriptive
Idea for a Norm
Descriptive
Observation of
consistent behavior
ESSAY

62. Prescriptive
Idea for a Norm
Descriptive
Observation of
consistent behavior
Requests for
Comments
ESSAY
rabble
rabble
rabble

63. Prescriptive
Idea for a Norm
Descriptive
Observation of
consistent behavior
Requests for
Comments
GUIDELINE
POLICY
ESSAY
rabble
rabble
rabble

64. GUIDELINE
POLICY
ESSAY
Informal Formalized
Morgan, J. T., & Zachry, M. (2010,
November). Negotiating with angry
mastodons: the wikipedia policy
environment as genre ecology. In
Proceedings of the 16th ACM
international conference on Supporting
group work (pp. 165-168). ACM.
Halfaker, A., Geiger, R. S., Morgan, J.
T., & Riedl, J. (2012). The rise and
decline of an open collaboration system:
How Wikipedia’s reaction to popularity is
causing its decline. American Behavioral
Scientist, 0002764212469365.

65. Informal Formalized

66. Growth of regulations
Halfaker, A., Geiger, R. S., Morgan, J. T., & Riedl, J. (2012). The rise and decline of an open collaboration system: How Wikipedia’s
reaction to popularity is causing its decline. American Behavioral Scientist, 0002764212469365.

67. Growth of regulations
Halfaker, A., Geiger, R. S., Morgan, J. T., & Riedl, J. (2012). The rise and decline of an open collaboration system: How Wikipedia’s
reaction to popularity is causing its decline. American Behavioral Scientist, 0002764212469365.

68. Growth of regulations
Halfaker, A., Geiger, R. S., Morgan, J. T., & Riedl, J. (2012). The rise and decline of an open collaboration system: How Wikipedia’s
reaction to popularity is causing its decline. American Behavioral Scientist, 0002764212469365.

69. Work allocation
● Identify, prioritize and assign tasks
Regulation of behavior
● Norm formation, propagation and enforcement
Quality control
● Identify and remove damage

70. Quality control

71. Quality control
!

72. Quality control
!
Fully automated
Machine learning
● Fast (~ 5 seconds)[1]
● No human effort
● Only obvious vandalism
Semi automated
Human computation
● Still pretty fast (~ 30
seconds)[1]
● Minimizes human effort
● Humans catch most
vandalism at a glance
1. R. Stuart Geiger & Aaron Halfaker. When the Levee Breaks (2013). WikiSym.

73. Quality control
!
Fully automated
Semi automated
Banning (Admins)

74. Quality control
!
Fully automated
Semi automated
Banning (Admins)
Innate
- Fast
- General
- Local
Adaptive
- Slow
- Specific
- Global
Halfaker, A. & Riedl, J. (2012) Bots and Cyborgs,
IEEE Computing 45(3) (p. 79-82)
Geiger, R. S., & Ribes, D. (2010, February). The
work of sustaining order in wikipedia: the banning
of a vandal. CSCW (pp. 117-126). ACM.

75. Work allocation
● Identify, prioritize and assign tasks
Regulation of behavior
● Norm formation, propagation and enforcement
Quality control
● Identify and remove damage
Community management
● Newcomer socialization, dispute mediation & training

76. https://commons.wikimedia.org/wiki/File:Flickr_-_Official_U.S._Navy_Imagery_-_Sailor%27s_daug
hter_operates_a_fire_hose_with_crew_member_assistance..jpg
6,000 newcomers per day

77. https://commons.wikimedia.org/wiki/File:Flickr_-_Official_U.S._Navy_Imagery_-_Sailor%27s_daug
hter_operates_a_fire_hose_with_crew_member_assistance..jpg
Host
Bot
vandals
good-faith
6,000 newcomers per day

78. https://commons.wikimedia.org/wiki/File:Flickr_-_Official_U.S._Navy_Imagery_-_Sailor%27s_daug
hter_operates_a_fire_hose_with_crew_member_assistance..jpg
Host
Bot
good-faith
vandals
6,000 newcomers per day

79. Work allocation
● Identify, prioritize and assign tasks
Regulation of behavior
● Form, propagate and enforce norms
Quality control
● Identify and remove damage
Community management
● Socialize & train newcomers; mediate disputes
Reflection (Adaptation)
● Where are we going? Where do we want to go? How do we want to get there?

80. Reflection (Adaptation)
Where are we going?
Where do we want to go?
How do we get there?
?

82. Reflection
Adaptation

83. https://commons.wikimedia.org/wiki/File:Morphine_to_Apomorphine.png CC-SY-SA 4.0

84. https://commons.wikimedia.org/wiki/File:Morphine_to_Apomorphine.png CC-SY-SA 4.0
https://commons.wikimedia.org/wiki/File:Gear_reducer.gif CC-BY-SA 3.0
Norms
User:ClueBot NG

85. Available
Human
Attention

86. Part 2. Critique of
algorithmic quality
control

87. ● :m:Research:The_Rise_and_Decline
● Halfaker, A., Geiger, R. S., Morgan, J. T., & Riedl, J. (2012). The rise and decline of an open collaboration
system: How Wikipedia’s reaction to popularity is causing its decline. American Behavioral Scientist,
0002764212469365.

89. https://commons.wikimedia.org/wiki/File:Flickr_-_Official_U.S._Navy_Imagery_-_Sailor%27s_daug
hter_operates_a_fire_hose_with_crew_member_assistance..jpg

90. User:ClueBot NG

91. The machine classifier
https://commons.wikimedia.org/wiki/File:Artificial.intelligence.jpg
Public Domain

92. The machine classifier
is_anon
chrs_added
chrs_removed
cust_comment
repeated_chrs
longest_token
badwords_added

93. The machine classifier
is_anon
chrs_added
chrs_removed
cust_comment
repeated_chrs
longest_token
badwords_added
?

94. BAD!
The machine classifier
is_anon
chrs_added
chrs_removed
cust_comment
repeated_chrs
longest_token
badwords_added
?
Good.

95. 160k
Edits
Per
Day
10%
90%
Need review
Probably OK
Counter vandalism

96. Without machine prediction: Reviewing 160k edits per day...
267 Hours
(33 people * 8 hours)
With machine prediction: Reviewing 16k edits per day…
27 Hours
(4 people * 8 hours)

97. User:ClueBot NG

98. What happened?

99. Standpoints & objectivity

100. Standpoints & objectivity
Donna Haraway
Via commons CC-By_SA 3.0

101. Standpoints & objectivity
Donna Haraway
Via commons CC-By_SA 3.0
Male scientists
Female* scientists

102. Standpoints & objectivity
Donna Haraway
Via commons CC-By_SA 3.0
Male scientists
Female* scientists

103. Standpoints & objectivity
Donna Haraway
Via commons CC-By_SA 3.0
Male scientists
Female* scientists

104. Standpoints & objectivity
Donna Haraway
Via commons CC-By_SA 3.0
Male scientists
Female* scientists

105. Standpoints & objectivity
Donna Haraway
Via commons CC-By_SA 3.0
Male scientists
Female* scientists
● reproductive
competition
● dominance
● communication
● social grooming

106. Standpoints & objectivity
Your standpoint gives you a view of what’s
important & valuable. (comm vs. dominance)

107. Standpoints & objectivity
Your standpoint gives you a view of what’s
important & valuable. (comm vs. dominance)
An objectivity is what you construct to reify
this value. (grooming patterns vs. sex partners)

108. Standpoints & objectivity
Your standpoint gives you a view of what’s
important & valuable. (comm vs. dominance)
An objectivity is what you construct to reify
this value. (grooming patterns vs. sex partners)
Standpoints and objectivities can be merged
and extended.

109. Standpoints & objectivity
Donna Haraway
Via commons CC-By_SA 3.0
Male scientists
Female* scientists
● reproductive
competition
● dominance
● communication
● social grooming
Expanded standpoints
Multiple objectivities
Expanded understanding

110. ● Wikipedia is a firehose
● Bad edits must be reverted
● Minimize effort wasted on
quality control work
Standpoint

111. ● Wikipedia is a firehose
● Bad edits must be reverted
● Minimize effort wasted on
quality control work ClueBot NG
Standpoint Objectivity

112. ● Wikipedia is a firehose
● Bad edits must be reverted
● Minimize effort wasted on
quality control work
MASSIVE SUCCESS!
Standpoint Objectivity
ClueBot NG

113. User:ClueBot NG
THE
INTERNET
RecentChanges
~90% reduction
in workload

114. 267
hours
Reviewing all of the edits
That’s 33 people working
8 hours each.
27
hours
Reviewing edits WITH
MACHINE LEARNING.
That’s 4 people working
7 hours each.

115. Woah woah woah! All the
newcomers are leaving!
Suh, B., et al. (2009). The singularity is not
near: slowing growth of Wikipedia. WikiSym.
ACM.

116. Woah woah woah! All the
newcomers are leaving!
Suh, B., et al. (2009). The singularity is not
near: slowing growth of Wikipedia. WikiSym.
ACM.
We forgot to design for
socializing newcomers!
Halfaker, A. et al. (2012) The Rise and
Decline of an Open Collaboration
System. American Behavioral
Scientist.

117. 8%
160k
2% Vandals
Doing fine90%
Newcomers who need help

118. Work allocation
Regulation of behavior
Quality control
Community management
Reflection (Adaptation)

119. Work allocation
Regulation of behavior
Quality control
Community management
Reflection (Adaptation)
Hey! Maybe we should also value newcomers
having a good experience -- and design for that.

120. ● Wikipedia is a firehose
● Bad edits must be reverted
● Minimize effort wasted on
quality control work

121. ● Wikipedia is a firehose ✓
● Bad edits must be reverted
● Minimize effort wasted on
quality control work

122. ● Wikipedia is a firehose ✓
● Bad edits must be reverted ✓
● Minimize effort wasted on
quality control work

123. ● Wikipedia is a firehose ✓
● Bad edits must be reverted ✓
● Minimize effort wasted on
quality control work ✓

124. ● Wikipedia is a firehose ✓
● Bad edits must be reverted ✓
● Minimize effort wasted on
quality control work ✓
● Socialize & train the newcomers!

125. ● More newcomers -- major Wikimedia
Foundation goal
● New spaces developed for supporting
newcomers
2012 - 2015 : The conversation incorporates
new standpoint
The Coop The Teahouse

126. Win?

127. User:ClueBot NG
THE
INTERNET
RecentChanges
~90% reduction
in workload

129. First, the sugar

130. Huggle is an amazing piece of software. I have no doubt it
represents the state of the art in distributed quality control. This
software and its users are responsible for critical work. It’s
developers and users are wonderful people. We owe them a lot
for their thankless work, so let me take this opportunity to say:
Thank You

131. Now for the medicine

132. Huggle circa 2009

133. Good
Bad
Huggle circa 2009

134. Good
Bad
Probably
bad
Probably
less bad
Huggle circa 2009

135. Huggle in 2015

136. Huggle in 2015Good Bad

137. Good Bad
Probably
bad
Probably
less bad
Huggle in 2015

138. Good Bad
Probably
bad
Probably
less bad
Welcome!

139. ● Quality control in Wikipedia still not designed
with newcomer socialization in mind

140. ● Quality control in Wikipedia still not designed
with newcomer socialization in mind
● Newcomers (especially those who don’t
conform) remain marginalized

141. ● Quality control in Wikipedia still not designed
with newcomer socialization in mind
● Newcomers (especially those who don’t
conform) remain marginalized
● Still not seeing gains in the retention of
good-faith newcomers

142. Why?

143. Why?
Why did we see changes to some of the sub-systems in
Wikipedia, but not others?

144. Part 3: Infrastructure for
socio-technical change

145. The machine classifier
https://commons.wikimedia.org/wiki/File:Artificial.intelligence.jpg
Public Domain

146. BAD!
The machine classifier
is_anon
chrs_added
chrs_removed
cust_comment
repeated_chrs
longest_token
badwords_added
?
Good.

147. User:ClueBot NG
?? ?

148. User:ClueBot NG
?? ?
Wikipedia is a firehose ✓
Bad edits must be reverted ✓
Minimize effort wasted on
quality control work ✓
Socialize & train the newcomers! ✓

149. User:ClueBot NG
?? ?
Wikipedia is a firehose ✓
Bad edits must be reverted ✓
Minimize effort wasted on
quality control work ✓
Socialize & train the newcomers! ✓
?

150. User:ClueBot NG
?? ?
Wikipedia is a firehose ✓
Bad edits must be reverted ✓
Minimize effort wasted on
quality control work ✓
Socialize & train the newcomers! ✓
?
● 20+ research papers on
Wikipedia damage detection
● Machine classification not
part of standard CS degree
● Many volunteer tool devs
don’t have a CS degree
anyway.
Labor intensive, performance
considerations, etc.

151. User:ClueBot NG
?? ?
Wikipedia is a firehose ✓
Bad edits must be reverted ✓
Minimize effort wasted on
quality control work ✓
Socialize & train the newcomers! ✓
?
● 20+ research papers on
Wikipedia damage detection
● Machine classification not
part of standard CS degree
● Many wiki-tool devs don’t
have a CS degree anyway.
Labor intensive, performance
considerations, etc.

152. User:ClueBot NG
?? ?
Authored by computer scientists with extensive skills in
machine learning and distributed systems.

154. 149F

155. 72F

156. Progress ----->
Energy
Better quality
control &
socialization
Building
a machine
learning
models
User:ClueBot NG

157. Progress ----->
Energy
Progress catalyst
User:ClueBot NG
Better quality
control &
socialization

158. Progress ----->
Energy
Progress catalyst
User:ClueBot NG
Better quality
control &
socialization

159. Scoring Platform
User:ClueBot NG
?? ?

160. Scoring Platform
?
User:ClueBot NG

161. ?
User:ClueBot NG
Host
Bot
Scoring Platform

162. ?
User:ClueBot NG
Host
Bot
Scoring Platform

163. http://ores.wmflabs.org/scores/enwiki/damaging/638307884

164. http://ores.wmflabs.org/scores/enwiki/damaging/638307884
“English Wikipedia”

165. http://ores.wmflabs.org/scores/enwiki/damaging/638307884
Is this edit damaging?

166. http://ores.wmflabs.org/scores/enwiki/damaging/638307884

167. http://ores.wmflabs.org/scores/enwiki/damaging/638307884

168. http://ores.wmflabs.org/scores/enwiki/damaging/638307884
"638307884": {
"prediction": false,
"probability": {
"false": 0.942,
"true": 0.058
}
}

169. http://ores.wmflabs.org/scores/enwiki/damaging/642215410
http://ores.wmflabs.org/scores/enwiki/damaging/638307884
"638307884": {
"prediction": false,
"probability": {
"false": 0.942,
"true": 0.058
}
}

170. "638307884": {
"prediction": false,
"probability": {
"false": 0.942,
"true": 0.058
}
}
http://ores.wmflabs.org/scores/enwiki/damaging/642215410
http://ores.wmflabs.org/scores/enwiki/damaging/638307884
"642215410": {
"prediction": true,
"probability": {
"false": 0.080,
"true": 0.920
}
}

171. ● Fast (median delay 0.5 seconds)
● Scale-able & redundant
● Comparable to the state-of-the-art

172. Progress ----->
Energy
~20,000 lines of code +
1 advanced degree in
Computer Science
Connect to
ORES via
HTTP
Better quality
control
User:ClueBot NG

173. ~20,000 lines of code +
1 advanced degree in
Computer Science
Connect to
ORES via
HTTP
User:ClueBot NG
Progress ----->
Energy

174. Progress ----->
Energy
~20,000 lines of code +
1 advanced degree in
Computer Science
Connect to
ORES via
HTTP
User:ClueBot NG

175. Progress ----->
Energy
~20,000 lines of code +
1 advanced degree in
Computer Science
Connect to
ORES via
HTTP
User:ClueBot NG
- 30 volunteer
developed tools
- 3 major Wikimedia
Product initiatives
- Some really cool
data science
projects!

176. Part 3.5: Feminist
inspiration
Critique → Design

177. “Subjective Algorithms”

178. “Subjective Algorithms”"algorithms, often aided
by big data, now make
decisions in subjective
realms where there is no
right decision, and no
anchor with which to
judge outcomes."
Tufekci, Z. (2015). Algorithms in our Midst: Information, Power
and Choice when Software is Everywhere. CSCW (pp.
1918-1918). ACM.
“Subjective algorithms”
https://commons.wikimedia.org/wiki/File:Zeynep_Tufekci_2_(5805394290).jpg

179. “Subjective Algorithms”
What is good? relevant?
important? desirable? valuable?
"algorithms, often aided
by big data, now make
decisions in subjective
realms where there is no
right decision, and no
anchor with which to
judge outcomes."
Tufekci, Z. (2015). Algorithms in our Midst: Information, Power
and Choice when Software is Everywhere. CSCW (pp.
1918-1918). ACM.
“Subjective algorithms”
https://commons.wikimedia.org/wiki/File:Zeynep_Tufekci_2_(5805394290).jpg

181. Who is allowed to participate? Who gets labeled “bad-faith”?
What types of contributions will be labeled “damaging”?

182. BAD!
is_anon
chrs_added
chrs_removed
cust_comment
repeated_chrs
longest_token
badwords_added
?
Good.

183. BAD!
and some important
good stuff
is_anon
chrs_added
chrs_removed
cust_comment
repeated_chrs
longest_token
badwords_added
?
Good.
and some important bad
stuff

184. Please exercise extreme caution to avoid
encoding racism or other biases into an AI
scheme. [...] Wnt (talk) 12:58, 20 February
2015 (UTC)
From Wikipedia:Wikipedia_Signpost/2015-02-18/Special_report

185. Please exercise extreme caution to avoid
encoding racism or other biases into an AI
scheme. [...] Wnt (talk) 12:58, 20 February
2015 (UTC)
From Wikipedia:Wikipedia_Signpost/2015-02-18/Special_report
?

186. Civil
Harassment
Please exercise extreme caution to avoid
encoding racism or other biases into an AI
scheme. [...] Wnt (talk) 12:58, 20 February
2015 (UTC)
From Wikipedia:Wikipedia_Signpost/2015-02-18/Special_report
?
and messages from South Africans

187. Two stories
Damaging edit
is_anon
chrs_added
chrs_removed
cust_comment
repeated_chrs
longest_token
badwords_added
?
Good edit

188. Two stories
Damaging edit
● The Italian word “ha”
● Anonymous editors
is_anon
chrs_added
chrs_removed
cust_comment
repeated_chrs
longest_token
badwords_added
?
Good edit

189. The Italian “ha”
Literally: Not a laughing matter

190. :it:Progetto:Patrolling/ORES
:m:Talk:ORES#Checklist for itwiki setup.

191. Damaging edit
...
informals_added
badwords_added
...
?
Good edit

192. Damaging edit
...
informals_added
badwords_added
...
?
Good edit
Badwords: Curse words, racial slurs and other offensive terminology
Informals: Casual speak that would be welcome on a talk page, but not within an
article.

193. Damaging edit
...
informals_added
badwords_added
...
?
Good edit
Badwords: Curse words, racial slurs and other offensive terminology
Informals: Casual speak that would be welcome on a talk page, but not within an
article.
For example "hello" or "hahaha".

194. Badwords: Curse words, racial slurs and other offensive terminology
Informals: Casual speak that would be welcome on a talk page, but not within an
article.
Damaging edit
...
informals_added
badwords_added
...
?
Good edit
For example "hello" or "hahaha".

195. revscoring/languages/english.py#L163 revscoring/languages/tests/test_english.py#L103

196. revscoring/languages/english.py#L163 revscoring/languages/tests/test_english.py#L103
“Ha” is laughing in English, but “Ha” is not laughing in Italian!

197. … <snip> ...

198. Anonymous editors

199. Anonymous editors
Otherwise, anons seemed to dominate false-positive reports from
every wiki

200. … maybe anons are really
bad.

201. … maybe anons are really
bad.
● Generally, anon edits are twice as likely to be
vandalism

202. … maybe anons are really
bad.
● Generally, anon edits are twice as likely to be
vandalism
● 90% of anonymous edits are good

203. https://ores.wmflabs.org/v2/scores/enwiki/damaging/642345235?feature.revision.user.is_anon=false

204. https://ores.wmflabs.org/v2/scores/enwiki/damaging/642345235?feature.revision.user.is_anon=false
{"prediction": false,
"probability": {"false": 0.656,
" true": 0.344}}

205. https://ores.wmflabs.org/v2/scores/enwiki/damaging/642345235?feature.revision.user.is_anon=false
{"prediction": false,
"probability": {"false": 0.656,
" true": 0.344}}
https://ores.wmflabs.org/v2/scores/enwiki/damaging/642345235?feature.revision.user.is_anon=true
{"prediction": false,
"probability": {"false": 0.541,
" true": 0.459}}

206. https://ores.wmflabs.org/v2/scores/enwiki/damaging/642345235?feature.revision.user.is_anon=false
{"prediction": false,
"probability": {"false": 0.656,
" true": 0.344}}
https://ores.wmflabs.org/v2/scores/enwiki/damaging/642345235?feature.revision.user.is_anon=true
{"prediction": false,
"probability": {"false": 0.541,
" true": 0.459}}
Just by being “anon”, we score this edit 11.5%
more likely to be damaging to the article.

207. Anon editor
{"feature.revision.user.is_anon": true,
"feature...seconds_since_registration": 0,
"feature.revision.user.has_advanced_rights": false,
"feature.revision.user.is_admin": false,
"feature.revision.user.is_bot": false,
"feature.revision.user.is_curator": false}
New editor (2h since registration)
{"feature.revision.user.is_anon": false,
"feature...seconds_since_registration": 18000,
"feature.revision.user.has_advanced_rights": false,
"feature.revision.user.is_admin": false,
"feature.revision.user.is_bot": false,
"feature.revision.user.is_curator": false}
User:EpochFail (8 years since registration)
{"feature.revision.user.is_anon": false,
"feature...seconds_since_registration": 257995021,
"feature.revision.user.has_advanced_rights": false,
"feature.revision.user.is_admin": false,
"feature.revision.user.is_bot": false,
"feature.revision.user.is_curator": false}
User classesModeling strategies
https://en.wikipedia.org/wiki/Gradient_boosting
https://en.wikipedia.org/wiki/Support_vector_machine#Linear_SVM

210. Gradient Boosting Linear SVMNatural
user
class

211. Gradient Boosting Linear SVMNatural
user
class

212. Gradient Boosting Linear SVMNatural
user
class

213. ???
Gradient Boosting Linear SVMNatural
user
class

214. OK. What if every edit was anon?

215. Gradient Boosting Linear SVMAnon
user
class

216. Gradient Boosting Linear SVMAnon
user
class

217. Gradient Boosting Linear SVMAnon
user
class

218. What if every edit were from a
newcomer?

219. Gradient Boosting Linear SVMNewbie
user
class

220. Gradient Boosting Linear SVMNewbie
user
class

221. Gradient Boosting Linear SVMNewbie
user
class

222. What if I (EpochFail) saved all the
edits?

223. Gradient Boosting Linear SVMEpochFail
user
class

224. Gradient Boosting Linear SVMEpochFail
user
class

225. Gradient Boosting Linear SVMEpochFail
user
class

226. Gradient Boosting Linear SVMEpochFail
user
class

227. Mwahahahaha!

228. Dec. 2015:
- Gradient boosting deployed

229. Dec. 2015:
- Gradient boosting deployed
Still needs work:
- Bias against anons/newcomers lessened,
but not gone
- New sources of signal
- HashingVectorization
- Probabilistic Context-free Grammars

230. 1. Wikipedia as a socio-technical system
○ Systems-thinking & Biological metaphors ~

231. 1. Wikipedia as a socio-technical system
○ Systems-thinking & Biological metaphors
2. Critique of algorithmic quality control
○ Standpoint epistemology
○ Encoding of ideology in technology
~

232. 1. Wikipedia as a socio-technical system
○ Systems-thinking & Biological metaphors
2. Critique of algorithmic quality control
○ Standpoint epistemology
○ Encoding of ideology in technology
3. Infrastructure for socio-technical change
○ “progress catalyst”
○ Hearing to speech vs. Speaking to be heard
○ The dangers of “subjective algorithms”
~

233. Thanks!
SCIENCE
SCIENCE
SCIENCE
SCIENCE
SCIENCE
SCIENCE SCIENCE
Props to my collaborators
● User:Ladsgroup
● User:Staeiou
● User:He7d3r
● User:とある白い猫
● User:Awight
● User:YuviPanda
● User:Danilo.mac
● User:Jtmorgan
● User:Aetilley
● User:Riedl
Aaron Halfaker
ahalfaker@wikimedia.org
enwp.org/User:EpochFail
https://twitter.com/halfak

234. ● Communities as living, adaptive systems
● Algorithms in social spaces
● Empowerment as technological intervention
● Machine learning support for collaborative
work