Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach

TL;DR

This paper introduces MLClean, a unified framework for data cleaning that integrates techniques from data management, machine learning fairness, and security to improve the accuracy, fairness, and robustness of models in Big Data-AI systems.

Contribution

It proposes a comprehensive, integrated data cleaning framework, MLClean, that unifies diverse preprocessing techniques for modern machine learning applications.

Findings

Identifies dependencies among data preprocessing techniques.

Demonstrates improved model accuracy and fairness with MLClean.

Bridges gaps between data management, fairness, and security communities.

Abstract

The wide use of machine learning is fundamentally changing the software development paradigm (a.k.a. Software 2.0) where data becomes a first-class citizen, on par with code. As machine learning is used in sensitive applications, it becomes imperative that the trained model is accurate, fair, and robust to attacks. While many techniques have been proposed to improve the model training process (in-processing approach) or the trained model itself (post-processing), we argue that the most effective method is to clean the root cause of error: the data the model is trained on (pre-processing). Historically, there are at least three research communities that have been separately studying this problem: data management, machine learning (model fairness), and security. Although a significant amount of research has been done by each community, ultimately the same datasets must be preprocessed, and there is little understanding how the techniques relate to each other and can possibly be integrated. We contend that it is time to extend the notion of data cleaning for modern machine learning needs. We identify dependencies among the data preprocessing techniques and propose MLClean, a unified data cleaning framework that integrates the techniques and helps train accurate and fair models. This work is part of a broader trend of Big data -- Artificial Intelligence (AI) integration.