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

By Ki Hyun Tae, Yuji Roh, Hyunsub Kim, Steven Euijong Whang. 2019

80 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Information Systems, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bo Li
2. Yuji Roh
3. Bowen Zhou
4. Jinfeng Yi
5. Steven Euijong Whang
6. Geon Heo
7. Shuai Di
8. Jingen Liu

In Management Science and Operations Research

1. Bo Li
2. Bowen Zhou
3. Geon Heo
4. Jinfeng Yi
5. Yuji Roh
6. Steven Euijong Whang
7. Peng Qi
8. Peter Christen

Citations in the corpus

• A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “More recently, MLCLEAN [166] has been proposed to integrate three data operations: traditional data cleaning, model unfairness mitigation where the goal is to remove data bias that causes model fairness, and data sanitization where the goal is to remove data poisoning.”
• Trustworthy AI: From Principles to Practices (2021, background): “Data Preprocessing Anomaly Detection [70, 81, 257, 316] Data Anonymozation [14, 37, 232, 232] Differential Privacy [107, 110, 177]”