Multigroup Robustness: Ensuring Subgroups Are Not Harmed by Unrelated Data Corruption

Core Concepts

Multigroup robust learning algorithms ensure that the effects of dataset corruption on every subpopulation-of-interest are bounded by the amount of corruption to data within that subpopulation, even when the data corruption is not distributed uniformly over subpopulations.

Abstract

The content discusses a new notion of data-aware robustness called "multigroup robustness" in machine learning. The key ideas are: Standard robust learning algorithms may fail to protect subgroups when data corruption is localized to specific partitions of the training dataset, rather than distributed uniformly. This is motivated by real-world scenarios where data collection processes can lead to uneven patterns of data corruption across subpopulations. Multigroup robustness ensures that the effects of dataset corruption on every subpopulation-of-interest are bounded by the amount of corruption to data within that subpopulation. This provides more meaningful robustness guarantees than standard approaches that are oblivious to how the data corruption and affected subpopulations are related. The authors establish a connection between multigroup fairness (e.g. multiaccuracy) and multigroup robustness. They show that algorithms satisfying multiaccuracy can also provide multigroup robustness guarantees, and that multiaccuracy is in fact necessary for any non-trivial multigroup robust algorithm. The authors present an efficient post-processing approach that can augment any existing learning algorithm to add both multigroup robustness and multiaccuracy guarantees, while preserving the performance of the original algorithm. Experiments on real-world census datasets demonstrate that the post-processing approach can protect against simple attacks on multigroup robustness without sacrificing accuracy.

Stats

The gap between standard distributional assumptions and practical dataset limitations has been well-studied in machine learning literature. Surveys may compromise answers from certain subpopulations due to response bias. When amassing internet data for training large models, certain sources can be less trustworthy or more toxic than others.

Quotes

"To address the shortcomings of real-world datasets, robust learning algorithms have been designed to overcome arbitrary and indiscriminate data corruption. However, practical processes of gathering data may lead to patterns of data corruption that are localized to specific partitions of the training dataset." "Motivated by critical applications where the learned model is deployed to make predictions about people from a rich collection of overlapping subpopulations, we initiate the study of multigroup robust algorithms whose robustness guarantees for each subpopulation only degrade with the amount of data corruption inside that subpopulation."

Key Insights Distilled From

Multigroup Robustness

by Lunjia Hu,Ch... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00614.pdf

Deeper Inquiries

How can multigroup robustness be extended to multi-class classification problems

Multigroup robustness can be extended to multi-class classification problems by considering multiple subpopulations or groups within each class. Instead of just focusing on binary labels, we can define subgroups based on different characteristics or attributes within each class. The notion of multigroup robustness can then be applied to ensure that the model's predictions are robust to data corruption or adversarial attacks within each subgroup. By considering the unique challenges and vulnerabilities of each subgroup, we can enhance the model's overall robustness in a multi-class classification setting.

How can the post-processing approach be adapted to provide multigroup robustness guarantees for the final actions or decisions, rather than just the outputted predictor

To adapt the post-processing approach to provide multigroup robustness guarantees for the final actions or decisions, we can modify the algorithm to optimize for specific decision-making criteria rather than just the predictor's output. This can involve incorporating constraints or objectives related to decision-making processes, such as fairness, equity, or ethical considerations, into the post-processing steps. By adjusting the optimization process to account for the impact of decisions on different subgroups, we can ensure that the final actions taken by the model are robust and equitable across diverse populations.

What other applications beyond machine learning could benefit from the notion of multigroup robustness

The notion of multigroup robustness can have applications beyond machine learning in various domains where decision-making processes impact different subpopulations or groups. For example: Healthcare: Ensuring that medical treatments or interventions are effective and safe for diverse patient populations, considering factors such as age, gender, ethnicity, and underlying health conditions. Finance: Developing financial products and services that are accessible and beneficial to individuals from different socio-economic backgrounds, ensuring fair and equitable outcomes for all customers. Education: Designing educational programs and interventions that cater to the diverse learning needs and preferences of students from various cultural or socio-economic backgrounds, promoting inclusive and effective learning environments. Public Policy: Implementing policies and initiatives that address the specific needs and challenges faced by different communities or demographic groups, promoting social equity and justice. By incorporating the principles of multigroup robustness into decision-making processes across these domains, we can enhance fairness, transparency, and accountability in the outcomes delivered to diverse populations.