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The wide use of machine learning is fundamentally changing the softwaredevelopment paradigm (a.k.a. Software 2.0) where data becomes a first-classcitizen, on par with code. As machine learning is used in sensitiveapplications, it becomes imperative that the trained model is accurate, fair,and robust to attacks. While many techniques have been proposed to improve themodel training process (in-processing approach) or the trained model itself(post-processing), we argue that the most effective method is to clean the rootcause of error: the data the model is trained on (pre-processing).Historically, there are at least three research communities that have beenseparately studying this problem: data management, machine learning (modelfairness), and security. Although a significant amount of research has beendone by each community, ultimately the same datasets must be preprocessed, andthere is little understanding how the techniques relate to each other and canpossibly be integrated. We contend that it is time to extend the notion of datacleaning for modern machine learning needs. We identify dependencies among thedata preprocessing techniques and propose MLClean, a unified data cleaningframework that integrates the techniques and helps train accurate and fairmodels. This work is part of a broader trend of Big data -- ArtificialIntelligence (AI) integration.

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