Algorithmic Fairness in Sequential Decision Making
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SUN_yis_IDSS_phd_thesis.pdf
Description
Thesis PDF
Size
3.92 MB
Format
Adobe PDF
Checksum (MD5)
d8f2d6b285a11eedb56ba5228f7b9916
Author(s)
Sun, Yi
Advisor(s)
Veeramachaneni, Kalyan
Date Issued
February 2023
Publisher
Massachusetts Institute of Technology
Abstract
Machine learning algorithms have been used in a wide range of applications, and there are growing concerns about the potential biases of those algorithms. While many solutions have been proposed for addressing biases in predictions from an algorithm, there is still a gap in translating predictions to a justified decision. Moreover, even a justified and fair decision could lead to undesirable consequences when decisions create a feedback effect. While numerous solutions have been proposed for achieving fairness in one-shot decision-making, there is a gap in investigating the long-term effects of sequential algorithmic decisions. In this thesis, we focus on studying algorithmic fairness in a sequential decision-making setting.
We first study how to translate model predictions to fair decisions. In particular, given predictions from black-box models (machine learning models or human experts), we propose an algorithm based on the classical learning-from-experts scheme to combine predictions and generate a fair and accurate decision. Our theoretical results show that approximate equalized odds can be achieved without sacrificing much regret. We also demonstrate the performance of the algorithm on real data sets commonly used by the fairness community.
In the second part of the thesis, we study if enforcing static fair decisions in the sequential setting could lead to long-term equality and improvement of disadvantaged groups under a feedback loop. In particular, we model the interaction between algorithmic decisions and underlying distribution using Markov Decision Model with general transition functions. We propose a new metric that measures the distributional impact of algorithmic decisions as measured by the change in distribution’s center, spread and shape. This metric categorizes the impact into within-group impact and between-group impact, where within-group impact measures how policies impact the distribution within a group, and between-group impact how policies impact the distributions of two population groups differently. Our results show that there is generally a trade-off between utility and between-group impact for threshold policies, and common fairness constraints could lead to "backfire effects" where the impact on groups could be disparate.
MIT Department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
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