Manipulation-Proof Machine Learning

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.

2019-07-01

2020-02-14

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

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.

See attached PDF

Randomization done in office by a computer

Individual-week

No

1500 individuals

21600 individual-weeks

Approximately 50 simple challenges and 10 complex challenges

Busara_Manipulation_PAP_v3.pdf

An increasing number of decisions are guided by machine learning algorithms. In many settings, from consumer credit to criminal justice, those decisions are made by applying an estimator to data on an individual's observed behavior. But when consequential decisions are encoded in rules, individuals may strategically alter their behavior to achieve desired outcomes. This paper develops a new class of estimator that is 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 show that decision rules estimated with our strategy-robust method outperform those based on standard supervised learning approaches.

Björkegren, D., Blumenstock, J. E., & Knight, S. (2020). Manipulation-Proof Machine Learning. ArXiv:2004.03865 [Cs, Econ]. http://arxiv.org/abs/2004.03865

https://arxiv.org/abs/2004.03865