Date: 27th Feb 2020
Presentation: CNeRG Weekly Reading Group

We discussed the following paper in the weekly reading group of CNeRG, IIT Kharagpur.

This paper studies the impact of applying fair machine learning on the population in money lending scenario. The authors have strongly questioned the long standing assumption on fair machine learning: “Applying fairness constraints on machine learning will improve the situation for the protected groups”. The paper shows that under different conditions, the fairness constraint may even cause harm to the protected group which it intends to protect. The authors have also emphasized on the requirement of domain-specific modeling of delayed impact of fair ML to better understand which fairness constraint(s) serves the best under different conditions.

We used the slides made available by the authors. We also made the following toy examples to make it easier for the audience to understand the concepts.

Example-1

Consider the set of credit scores as Χ = {‘low’, ‘high’}. Let ‘low’ and ‘high’ correspond to 100 and 500 scores. The success probability of ‘low’ and ‘high’ applicants are 0 and 1 correspondingly. If someone gets a loan from the bank and repays successfully then her score improves by 75 points and the bank gains 25 utility by earning the interest amount. On the other hand, if the loan is defaulted then the candidate is set to lose 150 points in credit score, and the bank is set to lose 100 points. Let the group-wise distribution of the population over scores are as given in the table below.

group\scores ‘low’ ‘high’ total
black 90 10 100
white 300 100 400

Let’s assume the bank has an upper limit on the number of loans they can issue.

Example-2

Consider the set of credit scores as Χ = {‘low’, ‘high’}. Let ‘low’ and ‘high’ correspond to 100 and 500 scores. The success probability of ‘low’ and ‘high’ applicants are 0 and 1 correspondingly. If someone gets a loan from the bank and repays successfully then her score improves by 75 points and the bank gains 25 utility for getting the interest amount. On the other hand, if the loan is defaulted then the candidate is set to lose 150 points in credit score, and the bank is set to lose 100 points. Let the group-wise distribution of the population over scores are as given in the table below.

group\scores ‘low’ ‘medium’ ‘high’ total
black 90 0 10 100
white 200 100 100 400

Let’s assume the bank has an upper limit on the number of loans they can issue.