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Fundamental Limits of Fairness Interventions in Machine Learning: Separating Aleatoric and Epistemic Discrimination


Core Concepts
The fairness Pareto frontier delineates the optimal performance achievable by a classifier under group fairness constraints, separating inherent biases in the data distribution (aleatoric discrimination) from biases introduced by algorithmic choices (epistemic discrimination).
Abstract
The paper introduces the concept of the fairness Pareto frontier, which characterizes the optimal accuracy-fairness trade-off for a given data distribution and group fairness constraints. This frontier separates aleatoric discrimination, which is inherent in the data, from epistemic discrimination, which is due to algorithmic choices. The authors first recast the fairness Pareto frontier in terms of the conditional distribution of predicted outcomes given true labels and group attributes. They then use Blackwell's results on comparing statistical experiments to precisely characterize this feasible set of conditional distributions. This allows them to formulate the fairness Pareto frontier as a convex optimization problem. However, directly solving this optimization problem is challenging, so the authors propose a greedy improvement algorithm that iteratively refines the approximation of the fairness Pareto frontier. They prove convergence guarantees for this algorithm. The authors apply their framework to benchmark existing group fairness interventions. They find that on standard datasets, state-of-the-art fairness interventions are effective at reducing epistemic discrimination, as their fairness-accuracy curves approach the fairness Pareto frontier. However, when data has disparate missing patterns across groups, aleatoric discrimination increases, diminishing the effectiveness of these fairness interventions. Overall, the fairness Pareto frontier provides a principled way to separate and quantify different sources of algorithmic discrimination, guiding the development of more effective fairness-enhancing strategies.
Stats
The number of positive-label data n+ s and negative-label data n- s for each group s do not depend on the classifier.
Quotes
"For a given data distribution, what is the best achievable performance (e.g., accuracy) under a set of group fairness constraints?" "Aleatoric discrimination captures inherent biases in the data distribution that can lead to unfair decisions in downstream tasks. Epistemic discrimination, in turn, is due to algorithmic choices made during model development and lack of knowledge about the optimal 'fair' predictive model."

Deeper Inquiries

How can the fairness Pareto frontier framework be extended to handle other types of data biases, such as measurement errors or self-reported attributes

The fairness Pareto frontier framework can be extended to handle other types of data biases, such as measurement errors or self-reported attributes, by incorporating these biases into the modeling process. For measurement errors, one approach could be to introduce uncertainty measures into the model that account for the potential inaccuracies in the data. This could involve modeling the measurement error distribution and incorporating it into the optimization process to ensure that the model is robust to these errors. Similarly, for self-reported attributes, the framework can be extended by considering the reliability and validity of the self-reported data. By quantifying the uncertainty associated with self-reported attributes, the fairness Pareto frontier can be adjusted to account for the potential biases introduced by self-reporting. This could involve incorporating additional constraints or regularization terms in the optimization process to mitigate the impact of unreliable self-reported data on the fairness-accuracy trade-off. In essence, extending the fairness Pareto frontier framework to handle other types of data biases involves incorporating the specific characteristics of the bias into the modeling process and adjusting the optimization criteria accordingly to ensure fair and accurate predictions despite the presence of these biases.

What are the implications of the fairness Pareto frontier for the design of fair data collection and curation processes

The implications of the fairness Pareto frontier for the design of fair data collection and curation processes are significant. By providing a benchmark for the highest achievable accuracy under a set of group fairness constraints, the fairness Pareto frontier can guide the data collection process to ensure that the collected data is representative and unbiased. One implication is that the fairness Pareto frontier can inform the selection of data sources and the design of data collection protocols to minimize biases in the dataset. By understanding the limits of fairness and accuracy given the data distribution, researchers and practitioners can prioritize data collection efforts that promote fairness and mitigate discrimination. Additionally, the insights from the fairness Pareto frontier can be used to identify potential sources of bias in the data and develop strategies to address them. For example, if the frontier indicates that certain population groups are disproportionately affected by missing values or measurement errors, efforts can be made to collect additional data or implement data cleaning techniques to reduce these biases. Overall, the fairness Pareto frontier can serve as a valuable tool for designing fair data collection and curation processes that prioritize equity, transparency, and accuracy in machine learning models.

How can the insights from the fairness Pareto frontier be used to develop new fairness interventions that are more effective at handling aleatoric discrimination

The insights from the fairness Pareto frontier can be used to develop new fairness interventions that are more effective at handling aleatoric discrimination by providing a benchmark for evaluating the performance of existing interventions and guiding the development of new strategies. One approach could be to use the fairness Pareto frontier to identify the specific sources of aleatoric discrimination in the data and tailor interventions to address these biases. For example, if the frontier indicates that certain subgroups are disproportionately affected by missing values, new interventions could focus on imputation techniques or data augmentation strategies to mitigate these biases. Furthermore, the frontier can be used to optimize the trade-off between fairness and accuracy by quantifying the gap between a model's performance and the optimal achievable accuracy under fairness constraints. This information can guide the development of new fairness interventions that strike a balance between fairness and accuracy, ensuring that models are both equitable and effective. Overall, the insights from the fairness Pareto frontier can inform the design and implementation of new fairness interventions that are tailored to the specific data biases present in the dataset, ultimately leading to more effective and equitable machine learning models.
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