presented at FITC Toronto 2018 More info at http://fitc.ca/event/to18/ Sara Simon, The New York Times, Interactive News Overview As technologists, we rely on a belief in rules and systems. We mold our work to fit between constraints. We operate under a set of defined assumptions. This is a story of assumptions upside down. This talk explores our common understanding of the algorithm. It’s a talk about how we talk about algorithms, and, more importantly, it’s a talk about the effects of this narrative. Objective The goal of this talk is to challenge the idea of the algorithm as something that’s mysterious, ambiguous, immutable and existing without human involvement. Target Audience This not a technical talk, though the audience should have a familiarity with technical topics. Five Things Audience Members Will Learn A little computer science history A not-too-technical dive into the mechanics of algorithms The importance of planning ahead The importance of learning to improvise The need to make interdisciplinary connections

1. the __ALGORITHM __
(a narrative)
sara simon (i work at the new york times)
@sarambsimon (that’s me on twitter)
fitc tech + creativity (twenty eighteen)
* **

2. interactive news
technologies

3. to find + tell the stories that
without software we could not find + tell
interactive news
technologies

4. ART SCHOOL
• theater
• creative writing

5. LIBERAL ARTS COLLEGE
• english

6. i had an idea
in my mind
of what it must
mean to write code

11. I WAS WRONG

12. there’s no
algorithm for
getting into tech

13. there’s no
algorithm for
getting into techNEWS

14. p a u s e

15. and that’s why we’re
here today

16. “algorithm”

17. it is _algorithms_ that DETERMINE

18. it is _algorithms_ that DETERMINE
it is _algorithms_ that DESCRIBE

19. it is _algorithms_ that DETERMINE
it is _algorithms_ that DESCRIBE
it is _algorithms_ that DEFINE

20. it is _algorithms_ that DETERMINE
it is _algorithms_ that DESCRIBE
it is _algorithms_ that DEFINE
it is _algorithms_ that DECIDE

21. itis_algorithms_thatDETERMINE
itis_algorithms_thatDESCRIBE
itis_algorithms_thatDEFINE
itis_algorithms_thatDECIDE

22. “good things
come from
incomprehension”
- maira kalman

23. “i didn’t quite
understand that,
but i think maybe
i liked it?”
“huh!”

25. “SOMETIMES YOUR IDEAS ARE BAD”

26. (which was kind
of the point?)

27. this is a talk
about algorithms

28. VERYLARGE

29. “[a]n algorithm is any well-
defined computational
procedure that takes some
value, or set of values, as
input and produces some value,
or set of values, as output.”

32. this talk explores
our common
understanding of
the algorithm

33. 1 computer science
background + history

34. 2 algorithms
in real life

35. 3 what we think about
when we hear the word
_algorithm_

36. this is a talk about
our narrative of the
algorithm

37. 1in definition

38. 1in definition 2 in practice

39. 1in definition 2
3in discussion
in practice

40. 1in definition 2
3in discussion 4
in practice
in transformation

42. “it is not the algorithm,
narrowly defined,
that has sociocultural effects,
but algorithmic systems —
intricate, dynamic arrangements of
people and code.
outside of textbooks, ‘algorithms’ are
almost always ‘algorithmic systems.’”

44. “the next time you hear someone
talking about algorithms,
replace the term with ‘god’ &
ask yourself if the
meaning changes”

45. the guardian
“what they watch is shaped by
this algorithm”
GOD

46. politico
“automation bias, he calls it:
the idea that people assume that
what an algorithm spits out must
be logical, right and good.”
GOD

47. the new york times magazine
“like a child who learns to ride a
bicycle by trial and error and,
asked to articulate the rules that
enable bicycle riding, simply shrugs
her shoulders and sails away…”

48. the new york times magazine
“…the algorithm looks vacantly at us
when we ask, ‘why?’ it is, like death,
another black box.”
GOD

49. “a thing you can hold in
your palm and caress.
a beautiful thing.
a divine one.”

50. “they have turned
into a new type
of theology”

51. WELLS FARGO

52. WALTER ISAACSON

53. 487/488
WALTER ISAACSON

55. the dangerous
mystique of the
algorithm

58. BAROMETERS

59. TELESCOPES

60. SPECTROSCOPES

61. THERMOSCOPES

63. “this is the finger
belonging to the
illustrious hand that
ran through the skies…”

64. none
of it is magic

65. “WHAT IS AN
ALGORITHM?”

66. 1) tynker
coding for kids

67. 1) tynker
coding for kids
not this ͢

68. 2) an explainer
from slate

69. 3) wikipedia

70. 4) a cute video
from khan academy
how to make a
grilled cheese sandwich

72. we think of a grilled
cheese sandwich as
something entirely
uncomplicated

73. if presence of breadbox is true:

74. if presence of breadbox is true:
open lid & remove bagged loaf

75. if presence of breadbox is true:
open lid & remove bagged loaf
else:

76. if presence of breadbox is true:
open lid & remove bagged loaf
else:
open refrigerator & remove bagged loaf

77. if twist tie:

78. if twist tie:
undo

79. remove loaf from bag

80. remove loaf from bag
find cerated knife

81. remove loaf from bag
find cerated knife
(preferably one that’s clean)

82. remove loaf from bag
find cerated knife
(preferably one that’s clean)
find cutting board

83. remove loaf from bag
find cerated knife
(preferably one that’s clean)
find cutting board
(preferably one that’s clean)

84. pull loaf from bag

85. pull loaf from bag
slice in two-inch-thick pieces

86. pull loaf from bag
slice in two-inch-thick pieces
(according to the user’s predetermined
center slice vs end of loaf slice
preferences)

87. place loaf remnants back in bag

88. place loaf remnants back in bag
place bag back in established bread area

89. if area was established as refrigerator:

90. if area was established as refrigerator:
keep door open to find cheese

91. if area was established as refrigerator:
keep door open to find cheese
(preferably a block of extra
sharp cheddar)

92. if area was established as refrigerator:
keep door open to find cheese
(preferably a block of extra
sharp cheddar)
(though others kinds will do)

93. place block of cheese on cutting board

94. place block of cheese on cutting board
remove wrapper

95. place block of cheese on cutting board
remove wrapper
use knife to slice cheese about 2/3rds
of an inch thick

96. place block of cheese on cutting board
remove wrapper
use knife to slice cheese about 2/3rds
of an inch thick
(enough slices to cover the area of
one side of bread)

97. YOU GET THE POINT

98. written precisely to be
processed by something
that has no concept of
food or food storage

99. “IT’S THE FAULT OF
THE RECIPE!”
- someone very quick to judge

100. APRIL 13, 1958

101. APRIL 13, 1958

102. APRIL 13, 1958

104. THE MUSIC MAN

105. THE MUSIC MAN
distinguished musical actor

106. THE MUSIC MAN
distinguished musical actor
distinguished supporting or featured musical actor

107. THE MUSIC MAN
distinguished musical actor
distinguished supporting or featured musical actor
distinguished supporting or featured musical actress

108. THE MUSIC MAN
distinguished musical actor
distinguished supporting or featured musical actor
distinguished supporting or featured musical actress
conductor and musical director

109. THE MUSIC MAN
distinguished musical actor
distinguished supporting or featured musical actor
distinguished supporting or featured musical actress
conductor and musical director
outstanding musical

110. THE MUSIC MAN
professor harold hill
a traveling salesman

111. a salesman
travels
from town to
town

112. a salesman
travels
from town to
town
what’s the
most efficient
route for him
to take?

115. “a weighted graph is
one whose edges have
a cost or distance
associated with them”

116. “an undirected graph is
one whose edges have a
bidirectional flow”

117. brute force

119. four

120. fourteen

121. four dozen

122. four hundred

123. four thousand

124. four
hundred
^
thousand

126. this is why developing
an algorithm to solve
the traveling salesman
problem is so hard

127. 🐝

128. everything can be
broken down into a
series of steps +
solutions

129. that’s just a fancy way to
say that my work is to use
logic + language to solve
puzzles

130. accountability
algorithmic

131. we’ve determined that
an algorithm is a
series of steps or
instructions

132. A LACK OF:
judgment

133. morals
A LACK OF:

134. ethics
A LACK OF:

135. privacy
A LACK OF:

136. some of
these or MANY
A LACK OF:

137. basic human rights
A LACK OF:

138. when i talk about a
life truly affected
by an algorithm?

139. the l.a. times
december 2017
“the los angeles police department
asked drivers to avoid navigation apps,
which are steering users onto more
open routes — in this case, streets
in the neighborhoods that are on fire.”

140. politico
january/february 2018
“the things fiscalnote is doing—
sifting through murky bills and
votes and patterns of behavior—
is precisely why you hire an
experienced staffer.”

141. the marshall project
august 2015

142. propublica
may 2016

143. wired
october 2017

144. wired
october 2017
“public agencies responsible for areas
such as criminal justice, health, and
welfare increasingly use scoring systems
and software to steer or make decisions
on life-changing events like granting bail,
sentencing, enforcement, and prioritizing
services.”

145. JOY BUOLAMWINI

146. SAFIYA UMOJA NOBLE

147. CATHY O’NEIL

148. SARA WACHTER-BOETTCHER

149. VIRGINIA EUBANKS

150. parents kim & kevin snipes
and their
daughter sophie

151. sophie was denied
eligibility and left
without care

152. “it would do this by automating
welfare eligibility processes:
substituting online applications for
face-to-face interactions, building
centralized call centers throughout
the state and ‘transitioning’ 1500
state employees to private telephone
call centers run by acs”

153. “between 2006 and 2008,
the state of indiana denied more
than a million applications for
food stamps, medicaid, and cash
benefits, a 54 percent increase
compared to the three years
prior to automation”

154. “hybrid eligibility system”

155. “automated eligibility was based
on the assumption that it is
better for ten eligible
applicants to be denied public
benefits than for one ineligible
person to receive them”

156. “coordinated-entry
system”

157. vulnerability index

158. who’s tasked with the
security of this database??

159. allegheny county, pennsylvania
a predictive risk model for child abuse and neglect

160. “there are all kinds of biases”

161. i was at a journalism conference
a few weeks ago
computer-assisted reporting

162. (1000+)

163. people who write code
in newsrooms

164. m a c h i n e
l e a r n i n g

165. “it’s so easy! just a
few lines of code!”

166. code is tremendously
hard for me

167. approximately
twelve men &
exactly zero
women line for
the microphone

168. “so this is really more of a
comment than a question”

169. “so this is really more of a
comment than a question”
(the whole room cringes)

170. “and there’s no way for me
to say this without sounding
like as ass”

171. “and there’s no way for me
to say this without sounding
like as ass”
(the room perks up?)

172. “one of my students got the
same scoop on your story…”

173. “and…”

174. “uh…”

175. “he did it without
machine learning.”

176. DO
NOT
give weight
to fancy
buzzwords

177. “a math person”

178. “i’m just not a
math person”

179. because
math is
not magic!

180. because
math is
not magic!
(neither is
computer
science)

181. because
math is
not magic!
(neither is
computer
science)
(nor is
machine
learning)

182. because
math is
not magic!
(neither is
computer
science)
(nor is
machine
learning)
(nor are algorithms)

183. it’s just hard

184. computers will do only
what we ask of them

185. algorithms are not
spooky

186. algorithms are not
one-size-fits-all

187. algorithms are not
ambiguous

188. they are mere math

189. they are patterns

190. they are dissectible

191. they are approachable

192. they are learnable

193. let’s be sure to talk
about them as such

194. thank you
sara simon (i work at the new york times)
@sarambsimon (that’s me on twitter)
fitc tech + creativity (twenty eighteen)