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Manipulation-Proof Machine Learning
Last registered on November 22, 2020
View Trial History

Pre-Trial

Trial Information
General Information
Title
Manipulation-Proof Machine Learning
RCT ID
AEARCTR-0004649
Initial registration date
August 30, 2019
Last updated
November 22, 2020 2:01 PM EST
Location(s)
Country
Kenya
Region
Nairobi
Primary Investigator
Name
Joshua Blumenstock
Affiliation
U.C. Berkeley
Contact Primary Investigator
Other Primary Investigator(s)
PI Name
Samsun Knight
PI Affiliation
Brown University
Contact Investigator
PI Name
Daniel Bjorkegren
PI Affiliation
Brown University
Contact Investigator
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, Joshua Blumenstock and Samsun Knight. 2020. "Manipulation-Proof Machine Learning." AEA RCT Registry. November 22. https://doi.org/10.1257/rct.4649-1.3.
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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
2020-02-14
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
See attached PDF
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
1500 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
Analysis Plan Documents
/versions/80123/docs/version/document

MD5:

SHA1:

Uploaded At: January 14, 2020

Busara_Manipulation_PAP_v3.pdf

MD5: 973080ba90348431af95fb0a9c870471

SHA1: 9a5674ed1d03588b615b358482add37c8b0e2529

Uploaded At: February 04, 2020

Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)
REPORTS & OTHER MATERIALS