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The Algorithm: A Narrative

presented at FITC Toronto 2018

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 learning to improvise
The need to make interdisciplinary connections

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The Algorithm: A Narrative

1. 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. 2. interactive news technologies
3. 3. to find + tell the stories that without software we could not find + tell interactive news technologies
4. 4. ART SCHOOL • theater • creative writing
5. 5. LIBERAL ARTS COLLEGE • english
6. 6. i had an idea in my mind of what it must mean to write code
7. 7. I WAS WRONG
8. 8. there’s no algorithm for getting into tech
9. 9. there’s no algorithm for getting into techNEWS
10. 10. p a u s e
11. 11. and that’s why we’re here today
12. 12. “algorithm”
13. 13. it is _algorithms_ that DETERMINE
14. 14. it is _algorithms_ that DETERMINE it is _algorithms_ that DESCRIBE
15. 15. it is _algorithms_ that DETERMINE it is _algorithms_ that DESCRIBE it is _algorithms_ that DEFINE
16. 16. it is _algorithms_ that DETERMINE it is _algorithms_ that DESCRIBE it is _algorithms_ that DEFINE it is _algorithms_ that DECIDE
17. 17. itis_algorithms_thatDETERMINE itis_algorithms_thatDESCRIBE itis_algorithms_thatDEFINE itis_algorithms_thatDECIDE
18. 18. “good things come from incomprehension” - maira kalman
19. 19. “i didn’t quite understand that, but i think maybe i liked it?” “huh!”
21. 21. (which was kind of the point?)
22. 22. this is a talk about algorithms
23. 23. VERYLARGE
24. 24. “[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.”
25. 25. this talk explores our common understanding of the algorithm
26. 26. 1 computer science background + history
27. 27. 2 algorithms in real life
28. 28. 3 what we think about when we hear the word _algorithm_
29. 29. this is a talk about our narrative of the algorithm
30. 30. 1in definition
31. 31. 1in definition 2 in practice
32. 32. 1in definition 2 3in discussion in practice
33. 33. 1in definition 2 3in discussion 4 in practice in transformation
34. 34. “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.’”
35. 35. “the next time you hear someone talking about algorithms, replace the term with ‘god’ & ask yourself if the meaning changes”
36. 36. the guardian “what they watch is shaped by this algorithm” GOD
37. 37. politico “automation bias, he calls it: the idea that people assume that what an algorithm spits out must be logical, right and good.” GOD
38. 38. 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…”
39. 39. the new york times magazine “…the algorithm looks vacantly at us when we ask, ‘why?’ it is, like death, another black box.” GOD
40. 40. “a thing you can hold in your palm and caress. a beautiful thing. a divine one.”
41. 41. “they have turned into a new type of theology”
42. 42. WELLS FARGO
43. 43. WALTER ISAACSON
44. 44. 487/488 WALTER ISAACSON
45. 45. the dangerous mystique of the algorithm
46. 46. BAROMETERS
47. 47. TELESCOPES
48. 48. SPECTROSCOPES
49. 49. THERMOSCOPES
50. 50. “this is the finger belonging to the illustrious hand that ran through the skies…”
51. 51. none of it is magic
52. 52. “WHAT IS AN ALGORITHM?”
53. 53. 1) tynker coding for kids
54. 54. 1) tynker coding for kids not this ͢
55. 55. 2) an explainer from slate
56. 56. 3) wikipedia
57. 57. 4) a cute video from khan academy how to make a grilled cheese sandwich
58. 58. we think of a grilled cheese sandwich as something entirely uncomplicated
59. 59. if presence of breadbox is true:
60. 60. if presence of breadbox is true: open lid & remove bagged loaf
61. 61. if presence of breadbox is true: open lid & remove bagged loaf else:
62. 62. if presence of breadbox is true: open lid & remove bagged loaf else: open refrigerator & remove bagged loaf
63. 63. if twist tie:
64. 64. if twist tie: undo
65. 65. remove loaf from bag
66. 66. remove loaf from bag find cerated knife
67. 67. remove loaf from bag find cerated knife (preferably one that’s clean)
68. 68. remove loaf from bag find cerated knife (preferably one that’s clean) find cutting board
69. 69. remove loaf from bag find cerated knife (preferably one that’s clean) find cutting board (preferably one that’s clean)
70. 70. pull loaf from bag
71. 71. pull loaf from bag slice in two-inch-thick pieces
72. 72. pull loaf from bag slice in two-inch-thick pieces (according to the user’s predetermined center slice vs end of loaf slice preferences)
73. 73. place loaf remnants back in bag
74. 74. place loaf remnants back in bag place bag back in established bread area
75. 75. if area was established as refrigerator:
76. 76. if area was established as refrigerator: keep door open to find cheese
77. 77. if area was established as refrigerator: keep door open to find cheese (preferably a block of extra sharp cheddar)
78. 78. if area was established as refrigerator: keep door open to find cheese (preferably a block of extra sharp cheddar) (though others kinds will do)
79. 79. place block of cheese on cutting board
80. 80. place block of cheese on cutting board remove wrapper
81. 81. place block of cheese on cutting board remove wrapper use knife to slice cheese about 2/3rds of an inch thick
82. 82. 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)
83. 83. YOU GET THE POINT
84. 84. written precisely to be processed by something that has no concept of food or food storage
85. 85. “IT’S THE FAULT OF THE RECIPE!” - someone very quick to judge
86. 86. APRIL 13, 1958
87. 87. APRIL 13, 1958
88. 88. APRIL 13, 1958
89. 89. THE MUSIC MAN
90. 90. THE MUSIC MAN distinguished musical actor
91. 91. THE MUSIC MAN distinguished musical actor distinguished supporting or featured musical actor
92. 92. THE MUSIC MAN distinguished musical actor distinguished supporting or featured musical actor distinguished supporting or featured musical actress
93. 93. THE MUSIC MAN distinguished musical actor distinguished supporting or featured musical actor distinguished supporting or featured musical actress conductor and musical director
94. 94. THE MUSIC MAN distinguished musical actor distinguished supporting or featured musical actor distinguished supporting or featured musical actress conductor and musical director outstanding musical
95. 95. THE MUSIC MAN professor harold hill a traveling salesman
96. 96. a salesman travels from town to town
97. 97. a salesman travels from town to town what’s the most efficient route for him to take?
98. 98. “a weighted graph is one whose edges have a cost or distance associated with them”
99. 99. “an undirected graph is one whose edges have a bidirectional flow”
100. 100. brute force
101. 101. four
102. 102. fourteen
103. 103. four dozen
104. 104. four hundred
105. 105. four thousand
106. 106. four hundred ^ thousand
107. 107. this is why developing an algorithm to solve the traveling salesman problem is so hard
108. 108. 🐝
109. 109. everything can be broken down into a series of steps + solutions
110. 110. that’s just a fancy way to say that my work is to use logic + language to solve puzzles
111. 111. accountability algorithmic
112. 112. we’ve determined that an algorithm is a series of steps or instructions
113. 113. A LACK OF: judgment
114. 114. morals A LACK OF:
115. 115. ethics A LACK OF:
116. 116. privacy A LACK OF:
117. 117. some of these or MANY A LACK OF:
118. 118. basic human rights A LACK OF:
119. 119. when i talk about a life truly affected by an algorithm?
120. 120. 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.”
121. 121. 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.”
122. 122. the marshall project august 2015
123. 123. propublica may 2016
124. 124. wired october 2017
125. 125. 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.”
126. 126. JOY BUOLAMWINI
127. 127. SAFIYA UMOJA NOBLE
128. 128. CATHY O’NEIL
129. 129. SARA WACHTER-BOETTCHER
130. 130. VIRGINIA EUBANKS
131. 131. parents kim & kevin snipes and their daughter sophie
132. 132. sophie was denied eligibility and left without care
133. 133. “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”
134. 134. “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”
135. 135. “hybrid eligibility system”
136. 136. “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”
137. 137. “coordinated-entry system”
138. 138. vulnerability index
139. 139. who’s tasked with the security of this database??
140. 140. allegheny county, pennsylvania a predictive risk model for child abuse and neglect
141. 141. “there are all kinds of biases”
142. 142. i was at a journalism conference a few weeks ago computer-assisted reporting
143. 143. (1000+)
144. 144. people who write code in newsrooms
145. 145. m a c h i n e l e a r n i n g
146. 146. “it’s so easy! just a few lines of code!”
147. 147. code is tremendously hard for me
148. 148. approximately twelve men & exactly zero women line for the microphone
149. 149. “so this is really more of a comment than a question”
150. 150. “so this is really more of a comment than a question” (the whole room cringes)
151. 151. “and there’s no way for me to say this without sounding like as ass”
152. 152. “and there’s no way for me to say this without sounding like as ass” (the room perks up?)
153. 153. “one of my students got the same scoop on your story…”
154. 154. “and…”
155. 155. “uh…”
156. 156. “he did it without machine learning.”
157. 157. DO NOT give weight to fancy buzzwords
158. 158. “a math person”
159. 159. “i’m just not a math person”
160. 160. because math is not magic!
161. 161. because math is not magic! (neither is computer science)
162. 162. because math is not magic! (neither is computer science) (nor is machine learning)
163. 163. because math is not magic! (neither is computer science) (nor is machine learning) (nor are algorithms)
164. 164. it’s just hard
165. 165. computers will do only what we ask of them
166. 166. algorithms are not spooky
167. 167. algorithms are not one-size-ﬁts-all
168. 168. algorithms are not ambiguous
169. 169. they are mere math
170. 170. they are patterns
171. 171. they are dissectible
172. 172. they are approachable
173. 173. they are learnable
174. 174. let’s be sure to talk about them as such
175. 175. thank you sara simon (i work at the new york times) @sarambsimon (that’s me on twitter) fitc tech + creativity (twenty eighteen)