CAREER: From Dirty Data to Fair Prediction: Data Preparation Framework for End-to-End Equitable Machine Learning

University Of California-Santa Barbara, Santa Barbara CA

Investigators

Abstract

In an era where artificial intelligence (AI) is becoming integrated into every facet of life, the need for AI systems that respect ethical expectations has never been more crucial. Modern AI algorithms learn from examples, and the creation of more ethical systems should start by supplying better examples to the algorithm. While collecting higher quality data is usually very expensive, improving the quality of data by making better choices in the data-preparation stage adds minimal extra cost. This research departs from the current focus of considering ethical goals in the training phase, which is merely a small part of the end-to-end data science lifecycle, and targets the data-preparation pipeline as a strategic opportunity for eliminating unwanted errors and bolstering desirable objectives. The project’s education outreach includes enhancing the understanding of ethical implications among AI students and the wider community and attracting and retraining talent for the AI field. This award centers around the critical question: What are the fundamental downstream costs arising from fairness-unaware data preparation and how can we move toward end-to-end fairness through improved data preparation? Employing an information-theoretic lens, the project will investigate how skewed information flows from the original, dirty data to the clean training set, to the trained prediction model through the data-preparation pipeline. Specifically, the project delves into prevalent real-world dataset problems, such as missing values, heterogeneity, and data imbalance, to examine how errors in the data can be either amplified or mitigated while handling these issues. Motivated by this analysis, the project will devise fairness-aware data imputation, data encoding, and data-balancing techniques that can attain end-to-end ethical goals more effectively and efficiently. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.