Results of the summarizing process for the arXiv paper: 2109.00545v2

This paper explores the use of fair representation to guarantee fairness for unknown prediction tasks and multiple fairness notions simultaneously. The authors consider seven group fairness notions that cover independence, separation, and calibration concepts. Despite the backdrop of fairness impossibility results, they explore approximate fairness and prove that fair representation guarantees fairness for a subset of tasks where the representation is discriminative. All seven group fairness notions are linearly controlled by the representation's fairness and discriminativeness. When an incompatibility exists between different notions, fair and discriminative representation satisfies all approximately. To learn both fair and discriminative representations, they propose using pretext loss to self-supervise learning important semantics and Maximum Mean Discrepancy as a fair regularizer in a constrained optimization problem solved using a two-player game approach. Experiments on tabular, image, and face datasets show downstream predictions become fairer for all seven group fairness notions using the learned representation. The authors' theoretical findings provide valid fairness guarantees for these experiments. Overall, this work provides insights into how to achieve approximate multiple group fairness in unknown prediction tasks with fair representations while addressing incompatibilities between different notions of group fairness.

• Fair representation can guarantee fairness for unknown prediction tasks and multiple fairness notions simultaneously.
• Seven group fairness notions are considered, covering independence, separation, and calibration concepts.
• Approximate fairness is explored and fair representation is proven to guarantee fairness for a subset of tasks where the representation is discriminative.
• All seven group fairness notions are linearly controlled by the representation's fairness and discriminativeness.
• When incompatibility exists between different notions, fair and discriminative representation satisfies all approximately.
• Pretext loss is proposed to self-supervise learning important semantics, while Maximum Mean Discrepancy serves as a fair regularizer in a constrained optimization problem solved using a two-player game approach to learn both fair and discriminative representations.
• Experiments on tabular, image, and face datasets show downstream predictions become fairer for all seven group fairness notions using the learned representation.
• Theoretical findings provide valid fairness guarantees for these experiments.

Exploring Fair Representation to Guarantee Multiple Group Fairness in Unknown Prediction Tasks

In recent years, there has been an increased focus on fairness in machine learning. In particular, the ability to guarantee fairness for unknown prediction tasks and multiple fairness notions simultaneously is of great interest. This research paper explores this concept by considering seven group fairness notions that cover independence, separation, and calibration concepts. Despite the backdrop of existing impossibility results regarding achieving perfect fairness, the authors explore approximate fairness and prove that fair representation guarantees fairness for a subset of tasks where the representation is discriminative.

Fair Representation as Linear Combination of Discriminativeness and Fairness

The authors demonstrate that all seven group fairness notions can be linearly controlled by the representation's degree of both discriminativeness and fairness. When incompatibility exists between different notions, fair and discriminative representations satisfy all approximately.

Learning Both Fair and Discriminative Representations

To learn both fair and discriminative representations at once, they propose using pretext loss to self-supervise learning important semantics while also using Maximum Mean Discrepancy (MMD) as a fair regularizer in a constrained optimization problem solved using a two-player game approach.

Experiments Show Improved Downstream Predictions with Learned Representations

Experiments conducted on tabular data sets such as Adult Census Income dataset; image datasets such as CIFAR-10; face datasets such as CelebA show downstream predictions become fairer for all seven group fairness notions when using the learned representation compared to when not using it.

Conclusion: Valid Fairness Guarantees Provided by Theoretical Findings

Overall, this work provides insights into how to achieve approximate multiple group fairness in unknown prediction tasks with fair representations while addressing incompatibilities between different notions of group fairness. The authors' theoretical findings provide valid guarantees for these experiments which are promising steps towards ensuring more equitable outcomes from AI systems going forward.