ABSTRACT

Massive amounts of information are transferred constantly between different domains in the form of data streams. Social networks, blogs, online businesses, and sensors all generate immense data streams. Such data streams are received in patterns that change over time. While this data can be assigned to specific categories, objects and events, their distribution is not constant. These categories are subject to distribution shifts. These distribution shifts are often due to the changes in the underlying environmental, geographical, economic, and cultural contexts. For example, the risks levels in loan applications have been subject to distribution shifts during the COVID-19 pandemic. This is because loan risks are based on factors associated to the applicants, such as employment status and income. Such factors are usually relatively stable, but have changed rapidly due to the economic impact of the pandemic. As a result, existing loan recommendation systems need to be adapted to limited examples. This project will develop open software to help users evaluate online fairness-in algorithms, mitigate potential biases, and examine utility-fairness trade-offs. It will implement two real-world applications: online crime event recognition from video data and online purchase behavior prediction from click-stream data. To amplify the impact of this project in research and education, this project will leverage STEM programs for students with diverse backgrounds, gender and race/ethnicity. This project includes activities including seminars, workshops, short courses, and research projects for students.

This project aims to develop a new and innovative paradigm for designing, implementing, and evaluating online fairness-aware Deep Learning (DL) models. Such models would be used for classification tasks in noisy and non-stationary data streams. This project will focus on five areas. First, the project will explore how to ensure a variety of fairness principles are incorporated in a DL model in online and non-stationary settings. The project will also look at how to identify a neural network architecture that will reflect the causal structure and be adaptive to distribution shifts. The project also looks at how the DL model will learn global initialization of primal parameters (associated with model accuracy) and dual parameters (associated with model fairness). Finally, the project looks at how to make online learning algorithms robust to uncertainties in model estimation of fairness and how to, ultimately, interpret the fairness of an online DL model. By bridging the areas of neural architecture search, online meta-learning, and fairness-aware deep learning techniques, this project advances state-of-the-art research in Fairness in AI. This project will offer the following innovations: (1) disentangle underlying sensitive and non-sensitive causal variables from raw features via causal representation learning; (2) identify adaptive architectures for data streams via differential architecture search; (3) learn effective initializations for both primal and dual model parameters in an online-within-online manner; (4) develop robust versions of the algorithms to deal with uncertainties in model fairness and tasks, and (5) identify the training examples and latent causal variables responsible for model adaption using local and global interpretations.

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.

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