The document describes a system called AutoMan that integrates human computation via Amazon Mechanical Turk (MTurk) into programming. AutoMan allows programmers to write functions that are implemented by having MTurk workers complete small tasks. It addresses challenges like ensuring quality work from workers and preventing gaming of the system. AutoMan manages pricing, timing of tasks, and number of workers to balance cost, speed and accuracy of results for user-defined functions.

1. Dan
Barowy,
Charlie
Curtsinger,
Emery
Berger,
Andrew
McGregor
Programming
with
People:
Integra(ng
Human-­‐Based
&
Digital
Computa(on

2. Computers
really good at
some tasks…
decoding human genome

3. designing your next car…

4. blood flow simulation…

5. (science)
blood flow simulation…

6. Not so good at
other tasks…

7. “is this a giraffe?”

8. isGiraffe(image)
(not a real function!)

9. Can we
“implement”
this function?
isGiraffe( )

10. Can we
“implement”
this function?
isGiraffe( )
We could just ask people!

11. isGiraffe( )
Find people via MTurk…

12. h"p://mturk.com
MTurk = Amazon’s
“Mechanical Turk”

13. A sample task.

14. Original “Mechanical
Turk”: 18th Century
chess machine (!)

15. The secret. Amazon’s
service also looks like a
computer but has
people inside.

16. Now we
implement
this function
with MTurk
workers…
isGiraffe( )
True

17. Q1: How
much is this
task worth?
isGiraffe( )
True

18. isGiraffe( )
True
Q2: How
much time
should it
take?

19. isGiraffe( )
False
Q3: What if
worker gets
it wrong?

20. Why would
a worker get
it wrong?
False

21. Incompetent or
lazy (“Homer”)
False

22. Bots (Internet + $ = Scammers)
False

23. Both just
guessing
answers.

24. Or…evil genius:
deliberately picks
wrong answer.
False

25. random adversary model
No need to worry
about evil geniuses:
we are fine with a

26. random adversary model
i.e., Homer & Bender = OK.

27. random adversary model
i.e., Homer & Bender = OK.
We rule Dr. Evil out. Why?

28. long-term financial incentive
MTurk tracks approval
rates and work records:

29. credentials limit Sybil attacks
Use of financial credentials
make it hard to gin up new
accounts, and

30. isGiraffe( )
?
But how do we know
any one person is not a
Homer or a Bender?

31. isGiraffe( )
Idea: vote. If both agree
on the answer, we’re
happy, right?

32. Pr[agree]
=
1/2
isGiraffe( )
Not so much. Random =
50% chance of agreement.

33. Pr[agree]
=
1/32
<
5%
BUT: can dramatically
reduce likelihood by
increasing # of workers.

34. Pr[agree]
=
k(1/k)n
(unanimous
agreement)
k = # choices, n = # workers.
(see paper for more details)

35. é choices
ê Pr[agree]
More choices = fewer people

36. isGiraffe( )
( )
AutoMan: DSL in Scala
– runs on any JVM.

37. isGiraffe( )
( )
Total $ for computation
AutoMan programmer-specified

38. isGiraffe( )
( )
Total $ for computation
Confidence level
(per function)
95%
(p < 0.05)
AutoMan programmer-specified

39. isGiraffe( )
US minimum wage,
Adaptive doubling
AutoMan ties pay and time;
Doubles both if no workers.

40. isGiraffe( )
US minimum wage,
Adaptive doubling
30s, $0.06 ($7.25 / 120)
Initially: all tasks 30 seconds.

41. isGiraffe( )
US minimum wage,
Adaptive doubling
prevents gaming
30s, $0.06 ($7.25 / 120)
60s, $0.12
No one shows up: both doubled.

42. You might think
this is exploitable:
just wait for jobs to
rise in price.

47. Strategy fails
when other
people are
around &
grab money.

48.
=
base
(Pavail)round
*
mul=plierround
E[gain]
Math: workers
should never wait.
Expected
earnings
after some #
of rounds…

49. =
base
(½)round
*
mul=plierround
E[gain]
Even odds
somebody
will take the
money…

50. E[gain]
=
base
(½)round
*
2round
Doubling
increases
wage after
each round…

51. =
base
E[gain]
Terms
dependent on
rounds
cancel out.

52. no incentive to wait
=
base
E[gain]

53. isGiraffe( )
True 95% confidence
AutoMan
manages
time, $,
quality.

54. How
many
giraffes
are
in
this
picture?
k = 3 choices!
AutoMan
handles
“radio
button”
questions

55. How
many
giraffes
are
in
this
picture?
k = 3 choices!Risk: Homer &
Bender always guess

56. How
many
giraffes
are
in
this
picture?
k = 3 choices!E.g., always choose
first option.

57. How
many
giraffes
are
in
this
picture?
k = 3 choices!
To combat this,
AutoMan randomizes
answers.

58. 25 choices!
Which
are
from
Sesame
Street?
Kermit
the
Frog
Spongebob
Squarepants
Cookie
Monster
The
Count
Oscar
the
Grouch
☐
☐
☐
☐
☐
“Checkbox” questions

59. Which
are
from
Sesame
Street?
Kermit
the
Frog
Spongebob
Squarepants
Cookie
Monster
The
Count
Oscar
the
Grouch
þ
þ
þ
þ
þ
25 choices!
Same risk: random respondents

60. Which
are
from
Sesame
Street?
Kermit
the
Frog
Spongebob
Squarepants
Cookie
Monster
The
Count
Oscar
the
Grouch
þ
þ
☐
þ
☐
25 choices!
AutoMan checks each randomly

61. What
does
this
license
plate
say?
36d choices!
XXXXXX
366 choices = !2176782336[A-Z0-9]{6}!
Last question category:
constrained free-text

62. Which
one
of
these
doesn’t
belong?
[95%
conf.]
AUTOMAN:
spawns
3
tasks
@
$0.06;
30s
work
t1
t2
t3
Example real execution

63. Which
one
of
these
doesn’t
belong?
[95%
conf.]
AUTOMAN:
spawns
3
tasks
@
$0.06;
30s
work
t1
t2
t3
1m
50s

64. Which
one
of
these
doesn’t
belong?
[95%
conf.]
AUTOMAN:
spawns
3
tasks
@
$0.06;
30s
work
t1
t2
t3
1m
50s
2m
30s

65. Which
one
of
these
doesn’t
belong?
[95%
conf.]
AUTOMAN:
spawns
3
tasks
@
$0.06;
30s
work
t1
t2
t3
1m
50s
2m
30s
2m
50s

66. Which
one
of
these
doesn’t
belong?
[95%
conf.]
AUTOMAN:
spawns
3
tasks
@
$0.06;
30s
work
t1
t2
t3
1m
50s
2m
30s
2m
50s
Inconclusive!

67. Which
one
of
these
doesn’t
belong?
[95%
conf.]
AUTOMAN:
spawns
3
more
tasks
t1
t2
t3
t4
t5
t6

68. Which
one
of
these
doesn’t
belong?
[95%
conf.]
AUTOMAN:
spawns
3
more
tasks
t1
t2
t3
t4
t5
t6
7m

69. Which
one
of
these
doesn’t
belong?
[95%
conf.]
AUTOMAN:
spawns
3
more
tasks
t1
t2
t3
t4
t5
t6
18m
50s
7m

70. Which
one
of
these
doesn’t
belong?
[95%
conf.]
AUTOMAN:
spawns
3
more
tasks
t1
t2
t3
t4
t5
t6
7m
18m
50s
51m
Timeout: double pay and time

71. Which
one
of
these
doesn’t
belong?
[95%
conf.]
AUTOMAN:
spawn
1
more
task
@
$0.12,
60s
work
t1
t2
t3
t4
t5
t6
t7

72. Which
one
of
these
doesn’t
belong?
[95%
conf.]
AUTOMAN:
spawn
1
more
task
@
$0.12,
60s
work
t1
t2
t3
t4
t5
t6
t7
1h
9m
50s;
cost
=
$0.36
AUTOMAN:
5
out
of
6
⇒
95%
confidence;
return

73. read_plate( )
More sophisticated function:

74. 12.2%
[Maryland
State
Highway
Administra=on]
Success rate of real system!

75. Easy under optimal conditions

76. More complex in general

77. “Difficult” set of plates

78. Easier to read than CAPTCHAs!

79. Real task as posted on MTurk

80. Workflow: pictures to strings

81. def is_car(img_url: String) =
a.RadioButtonQuestion { q =>
q.budget = 1.00
q.confidence = 0.95
q.text = “Is this a car?”
q.image_url = img_url
q.options = List(
a.Option('yes, ”Yes"),
a.Option('no, ”No”)
)
}
Actual AutoMan code:

82. def get_plate_text(img_url: String) =
a.FreeTextQuestion { q =>
q.text = ”What does this plate
say?"
q.image_url = img_url
q.pattern = "XXXXXYYY”
}
Actual AutoMan code:

83. t1 t2 t3 t3 t4 t5 t6 t7 t8
"Is this a vehicle?"
start end
$0.06
post tasks
w1:
yes
w2:
yes
w3:
yes
3 answers
w4:
yes
1 answer
w5:
yes
Task 1
Task 2
Task 3
Task 4
Task 5
1 answer
"What does the license plate say?"
unanimous
agreement!
post tasks
$0.06
workers
disagree!
2 answers post tasks
Task 8
Task 9
timeout!
$0.12$0.06
post tasks
X
cancelled!
1 answer
end
767JKF
yes
w6:
767JFK
w7:
767JKF
Task 6
Task 7
w8:
767JKF
Task
10
Task
11
Example execution

84. MediaLab
LPR
database
“extremely
dif-icult”
dataset
144
plates
Accuracy:
91.6%
Average
cost:
12.08
cents
Latency:
<
2
minutes
per
image
>12.2%!
AutoMan evaluation

85. www.automan-­‐lang.org
AUTOMAN:
Programming
with
People
read_plate( )
def read_plate(url:
String) =
a.FreeTextQuestion { q =>
q.text = ”What does this
plate say?”
q.image_url = url
q.pattern = "XXXXXYYY”
}
Dan
Barowy,
Charlie
Curtsinger,
Emery
Berger,
Andrew
McGregor