  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.
