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Manipulation-Proof Machine Learning
Last registered on September 03, 2019
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Pre-Trial

Trial Information
General Information
Title
Manipulation-Proof Machine Learning
RCT ID
AEARCTR-0004649
Initial registration date
August 30, 2019
Last updated
September 03, 2019 1:39 PM EDT
Location(s)

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Primary Investigator
Name
Joshua Blumenstock
Affiliation
U.C. Berkeley
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Other Primary Investigator(s)
PI Name
Daniel Bjorkegren
PI Affiliation
Brown University
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Additional Trial Information
Status
On going
Start date
2019-07-01
End date
2021-01-01
Keywords
Welfare
Additional Keywords
Machine learning
JEL code(s)
Secondary IDs
Abstract
An increasing number of decisions are guided by machine learning algorithms. An individual’s behavior is typically used as input to an estimator that determines future decisions. But when an estimator is used to allocate resources, individuals may strategically alter their behavior to achieve a desired outcome. This paper develops a new class of estimators that are stable under manipulation, even when the decision rule is fully transparent. We explicitly model the costs of manipulating different behaviors, and identify decision rules that are stable in equilibrium. Through a large field experiment in Kenya, we test decision rules estimated with our strategy-robust method.
External Link(s)
Registration Citation
Citation
Bjorkegren, Daniel and Joshua Blumenstock. 2019. "Manipulation-Proof Machine Learning." AEA RCT Registry. September 03. https://doi.org/10.1257/rct.4649-1.0.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
The subject population consists of Kenyans aged 18 years or older who own a smartphone and are able to travel to the Busara center in Nairobi. A baseline survey will be conducted with all consenting participants in the Busara offices in Nairobi. At the time of enrollment, participants will install a "Sensing" app on their smartphone. More details on the app and the baseline survey are provided below.

While enrolled in the study, each participant will be dynamically assigned to a treatment for each week. Users will be sent both a text message (SMS) and an app push-notification on a weekly basis that directs them to the app. After a user opens the app, it will ask them to opt in to a "challenge". If they accept, they will observe a challenge which provides them some monetary incentive to change their behavior, as observed through the Sensing app.
Intervention Start Date
2019-07-01
Intervention End Date
2019-10-01
Primary Outcomes
Primary Outcomes (end points)
Note that our RCT is not standard in the sense that our primary objective is not to estimate the average treatment effect of an intervention on primary and secondary outcomes. Instead, the RCT is designed to allow us to estimate:
- The cost of manipulating different behaviors, as observed through the Sensing app (e.g., making a phone call, using multiple WiFi hotspots)
- The ease with which subjects can "game" algorithmic decision rules
- Whether transparent decision rules are easier to game than opaque ones
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The subject population consists of Kenyans aged 18 years or older who own a smartphone and are able to travel to the Busara center in Nairobi. We anticipate recruiting roughly 1,200 subjects for the main study, primarily through in-person solicitation in public spaces (e.g., public markets) by Busara research staff.

Informed consent is requested from each subject, following the procedures registered with the Committee for the Protection of Human Subjects at U.C. Berkeley. Subjects must actively decide to participate in the research; no pressure or undue influence will be given to induce subjects to participate.

A baseline survey will be conducted with all consenting participants in the Busara offices in Nairobi. At the time of enrollment, participants will install a "Sensing" app on their smartphone. While enrolled in the study, each participant will be dynamically assigned to a treatment for each week. Users will be sent both a text message (SMS) and an app push-notification on a weekly basis that directs them to the app. After a user opens the app, it will ask them to opt in to a "challenge". These challenges may use either a naive or manipulation-proof decision rule, and we will experimentally vary the level of transparency of the algorithm to the subject.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Individual-week
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
1200 individuals
Sample size: planned number of observations
21600 individual-weeks
Sample size (or number of clusters) by treatment arms
Approximately 50 simple challenges and 10 complex challenges
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
U.C. Berkeley Committee for the Protection of Human Subjects
IRB Approval Date
2018-07-19
IRB Approval Number
041671-001
IRB Name
Kenya Medical Research Institute
IRB Approval Date
2018-08-17
IRB Approval Number
624
Analysis Plan

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