Using fair machine learning to measure the welfare impacts of mobile banking in Africa
Joshua Blumenstock, Professor
Information, School of
Closed. This professor is continuing with Fall 2023 apprentices on this project; no new apprentices needed for Spring 2024.
Access to credit can be an important lifeline, but it can also lock people into debt traps. Mobile banking technologies have completely transformed the ways in which people, especially in the developing world, access credit, raising new questions about fairness and welfare in lending.
The goal of this project is to learn about the mechanisms, contexts and policies through which access to credit can improve welfare. Using data on millions of loans and loan applications done using mobile devices in Kenya (and potentially other populations), we will try to learn patterns and factors leading to improvements in well-being.
This project will therefore seek to answer a number of different but related questions:
1. What is well-being in this context? How can it be quantified?
2. What is harm and how can it be quantified?
3. What is fair in this context and can fairness be quantified?
4. From a descriptive standpoint, what factors predict increases in well-being?
5. What policies will properly balance profit and welfare?
To answer these, we will have to draw on modern research in fair algorithms, machine learning, causal inference and optimization. It is likely a large portion of this research will entail the derivation of new inferential methods.
Role: The undergraduate apprentice will have the opportunity to play an important role in real research from the very start of a new project. The beginning stage of this research will require deep exploration of the data; the apprentice should expect to spend time not simply cleaning data and outputting summary statistics, but deriving insights from the patterns they uncover.
Depending on the student’s interest, they may also participate in a thorough literature review of the field of algorithmic fairness, and help develop a framework for measuring fair lending.
In this project, the apprentice should expect to complete well-defined research tasks assigned to them in a timely manner. That being said, the hope is also that the student will propose insights and lines of inquiry of their own.
Qualifications: ● Proficiency in R or Python (required)
● Experience with large datasets (required)
● Ability to extract relevant information from data and present it clearly, especially through visualization (required)
● Knowledge of East African politics and society (preferred)
● Good understanding of basics of banks, finance and credit (preferred)
Day-to-day supervisor for this project: Jacqueline Mauro, Post-Doc
Hours: to be negotiated
Related website: https://sites.google.com/view/jacquelinemauro/home