Evolutionary Multi-Objective Optimization for Fairness-Aware Self-Adjusting Memory Classifiers in Data Streams

Core Concepts

This paper introduces a novel approach, Evolutionary Multi-Objective Optimization for Fairness-Aware Self-Adjusting Memory Classifiers (EMOSAM), designed to enhance fairness in machine learning algorithms applied to data stream classification.

Abstract

The paper proposes a new algorithm called EMOSAM that combines the strengths of the self-adjusting memory K-Nearest-Neighbor (SAMKNN) algorithm with evolutionary multi-objective optimization (EMO) to address fairness in data stream classification. Key highlights: EMOSAM integrates SAMKNN, which is effective in managing concept drift in streaming data, with EMO to optimize feature weights that maximize accuracy and minimize discrimination simultaneously. The paper introduces strategies to trigger EMO and adapt SAMKNN classifiers by selecting non-dominated solutions obtained by EMO. Extensive experiments on various datasets show that EMOSAM maintains competitive accuracy and significantly reduces discrimination compared to several baseline methods, highlighting its potential as a robust solution for fairness-aware data stream classification. The ablation study confirms the effectiveness of the proposed strategies for feature weight selection and EMO triggering.

Stats

EMOSAM maintains 3-12% lower discrimination than fairness-unaware methods (HT, HAT, SAM) across the datasets. EMOSAM achieves 6-10% higher accuracy than the fairness-aware method FABBOO on the Dutch and Census datasets. EMOSAM's majority voting approach for feature weight selection reduces model error by up to 2% and discrimination by up to 3.5% compared to alternative methods.

Quotes

"This paper introduces a novel approach, evolutionary multi-objective optimisation for fairness-aware self-adjusting memory classifiers, designed to enhance fairness in machine learning algorithms applied to data stream classification." "The proposed approach addresses this challenge by integrating the strengths of the self-adjusting memory K-Nearest-Neighbour algorithm with evolutionary multi-objective optimisation."

Key Insights Distilled From

Evolutionary Multi-Objective Optimisation for Fairness-Aware Self Adjusting Memory Classifiers in Data Streams

by Pivithuru Th... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.12076.pdf

Deeper Inquiries

How can the proposed EMOSAM approach be extended to handle more complex fairness criteria beyond statistical parity, such as equalized odds or demographic parity

The EMOSAM approach can be extended to handle more complex fairness criteria beyond statistical parity by incorporating additional fairness metrics into the multi-objective optimisation process. For example, to address equalized odds, the optimisation algorithm can be modified to simultaneously minimise the difference in true positive rates and false positive rates across different demographic groups. This would involve defining new objectives related to these metrics and adjusting the optimisation process to find a balance between accuracy, statistical parity, and equalized odds. Similarly, for demographic parity, the feature weighting process can be enhanced to ensure that the distribution of predictions is consistent across different demographic groups. By including these additional fairness criteria as objectives in the optimisation process, EMOSAM can be tailored to address a wider range of fairness concerns in machine learning algorithms applied to data streams.

What are the potential limitations of the EMOSAM approach in terms of computational complexity and scalability, and how can these be addressed

One potential limitation of the EMOSAM approach is its computational complexity, especially when dealing with large-scale datasets and high-dimensional feature spaces. As the number of features and instances in the data stream increases, the optimisation process may become more computationally intensive, leading to longer processing times and higher resource requirements. To address this limitation, techniques such as parallel processing, distributed computing, and algorithmic optimizations can be employed to improve the efficiency of the EMOSAM approach. Additionally, feature selection or dimensionality reduction methods can be applied to reduce the complexity of the feature space and streamline the optimisation process. By implementing these strategies, the computational burden of EMOSAM can be mitigated, making it more scalable and practical for real-world applications.

How can the insights from this work on fairness-aware data stream classification be applied to other domains, such as online recommendation systems or dynamic pricing, where fairness is also a critical concern

The insights from this work on fairness-aware data stream classification can be applied to other domains, such as online recommendation systems or dynamic pricing, where fairness is also a critical concern. In online recommendation systems, the EMOSAM approach can be adapted to ensure that recommendations are fair and unbiased across different user groups, considering factors like age, gender, or preferences. By incorporating fairness objectives into the recommendation algorithm and leveraging multi-objective optimisation techniques, the system can provide personalized recommendations while maintaining fairness and equity. Similarly, in dynamic pricing applications, EMOSAM can be used to optimize pricing strategies that are both accurate and fair, taking into account factors like income levels, location, or purchasing history. By integrating fairness considerations into the pricing model and using multi-objective optimisation to balance competing objectives, businesses can offer dynamic pricing that is transparent, ethical, and non-discriminatory.