Fairness-aware machine learning has gained significant attention due to the growing demand for ethical decision-support systems. This paper introduces FairRegBoost, a novel fairness-aware regression framework that takes a holistic data management perspective by integrating automated data preparation, uncertainty modeling, and post-processing adjustments using optimal transport techniques into effective and efficient solutions. Our approach effectively balances predictive accuracy and fairness by minimizing the output distribution distance between protected groups, leveraging uncertainty and sample similarities guiding the transport. We conduct extensive experiments on real-world datasets with both single and multiple protected attributes. Results demonstrate that FairRegBoost consistently achieves superior fairness-accuracy trade-offs compared to state-of-the-art approaches. Moreover, our scalability analysis highlights the computational efficiency, making it a practical choice for large-scale applications.

The problem of biased machine learning predictions has led to many alternative approaches to mitigate the problem. They are typically studied and evaluated by focusing on the input data, the trained model, and the performance of the model predictions. We take a broader perspective, considering approaches in the context of a multi-step pipeline. We study fair classification in a pipeline comprising multiple data preparation steps, parameter optimization, and three types of approaches (pre-, in-, and post-processing) designed to reduce bias that may be applied consecutively. This pipeline leads to a trained model to be evaluated in terms of quality (e.g., accuracy) and fairness. We experimentally evaluate the effect differently combined implementations of the pipeline components have on the performance of more than 40 fairness-inducing algorithms. Key findings made possible by this pipeline perspective include: (1) Choosing a bias reducing algorithm greatly simplifies when implementing suited data preparation or parameter optimization, as the difference in performance between methods shrinks, making almost any choice a good one. (2) Several component or pipeline implementations often assumed to have positive or negative effects on performance prove to have little or even contrary effects to the expectations. (3) While many approaches have been published for fair classification in the last decade and shown to improve on previous solutions in specific settings, our broad analysis reveals a stagnating performance trend. Our analysis shows that synergetic effects between pipeline components need to be carefully taken into account for further research on fair end-to-end data processing. It further raises the more fundamental question of how the study of the problem evolves, both in terms of proposed solutions and benchmarking.

A persistent problem of machine learning (ML) predictions is potential discrimination towards individuals from specific population groups, e.g., based on gender, religion, etc. Numerous algorithms have been proposed to tackle biased predictions leading to such discrimination, particularly for classification problems. These algorithms typically aim to reduce bias as defined by specific metrics. Given the large variety of algorithms and metrics, selecting a method suited for a particular application is tedious and challenging. FairCR is an extensible system that allows the evaluation of fair classification algorithms in a systematic and unified way. It further recommends which fair classification algorithms to use based on several application preferences. We showcase FairCR's functionality on a large set of readily implemented algorithms and metrics over multiple datasets, demonstrating how it can support the comparative evaluation of algorithms and help select specific fair classification alaorithms for given application preferences.

Machine learning (ML) models are frequently used for decision support. Typically, ML approaches are able to give decisions in an efficient way. They are mainly used to optimize prediction accuracy. However, from a legal and ethical standpoint, performing fair predictions should also be aimed for in a variety of applications. Unfair training datasets are a main reason that causes discrimination in supervised learning. If models are trained on unfair datasets, while ignoring underlying discrimination, they will learn to discriminate towards people of specific gender or ethnicity. In recent years, many different notions of fairness, algorithms, and systems have emerged to tackle the issue for different types of decision support problems, like classification, regression, or ranking.This paper presents ongoing and planned research to further advance this research field. First, we introduce a novel fairness definition called local fairness. It unifies ideas of both (global) group and individual fairness. Next, we summarize our initial FALCES system framework that aims at improving local fairness in classification problems without compromising accuracy. To cover realistic practical applications, improvements on both efficiency and effectiveness have been introduced, resulting in the FALCC system framework. This framework also paves the way towards a simplified configuration of the overall system. In the future, we plan to investigate how to make the system auto-configurable, leveraging AutoML techniques.