Exploring the Privacy-Fairness Tradeoff in Machine Learning

In this article, the authors explore the intersection of fairness and privacy in machine learning, specifically focusing on explainable artificial intelligence (XAI) under limited display settings. They propose a new approach called "k-RR," which combines the benefits of randomized response (RR) mechanisms with the interpretability of causal inference to address these challenges.
The authors begin by highlighting the issue of fairness in machine learning, particularly in scenarios where sensitive attributes are involved. They explain that traditional fairness metrics, such as demographic parity or equalized odds, can be compromised when dealing with privacy-sensitive data. To address this problem, they propose using causal inference to reason about the causal effect of a machine learning model on a protected attribute while ensuring privacy.
The authors then dive into the details of their proposed approach, k-RR. They explain that k-RR works by randomly obfuscating sensitive attributes in the data, allowing for fair inference while maintaining privacy. They also discuss how the privacy level can be adjusted based on the desired level of fairness, providing a tradeoff between the two.
The authors then demonstrate the effectiveness of their approach using several synthetic and real-world datasets. They show that k-RR can achieve fairness metrics comparable to those of unobfuscated models while maintaining privacy. They also provide analysis on how different settings, such as the privacy level and the number of obfuscation steps, impact the results.
Finally, the authors conclude by highlighting the potential applications of their approach in real-world scenarios, such as medical diagnosis or financial lending. They note that k-RR provides a much-needed solution for balancing fairness and privacy in machine learning, particularly in situations where data is sensitive and privacy must be protected.
In summary, the article proposes a new approach to addressing fairness and privacy in machine learning, using a combination of randomized response mechanisms and causal inference. The proposed approach, k-RR, maintains privacy while ensuring fairness metrics are met, providing a much-needed solution for real-world scenarios where data sensitivity and privacy must be protected.