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Fairness via Representation Neutralization
Mengnan Du · Subhabrata Mukherjee · Guanchu Wang · Ruixiang Tang · Ahmed Awadallah · Xia Hu

Thu Dec 09 04:30 PM -- 06:00 PM (PST) @

Existing bias mitigation methods for DNN models primarily work on learning debiased encoders. This process not only requires a lot of instance-level annotations for sensitive attributes, it also does not guarantee that all fairness sensitive information has been removed from the encoder. To address these limitations, we explore the following research question: Can we reduce the discrimination of DNN models by only debiasing the classification head, even with biased representations as inputs? To this end, we propose a new mitigation technique, namely, Representation Neutralization for Fairness (RNF) that achieves fairness by debiasing only the task-specific classification head of DNN models. To this end, we leverage samples with the same ground-truth label but different sensitive attributes, and use their neutralized representations to train the classification head of the DNN model. The key idea of RNF is to discourage the classification head from capturing spurious correlation between fairness sensitive information in encoder representations with specific class labels. To address low-resource settings with no access to sensitive attribute annotations, we leverage a bias-amplified model to generate proxy annotations for sensitive attributes. Experimental results over several benchmark datasets demonstrate our RNF framework to effectively reduce discrimination of DNN models with minimal degradation in task-specific performance.

Author Information

Mengnan Du (Texas A&M University)
Subhabrata Mukherjee (Microsoft Research)

Principal Researcher at Microsoft Research leading cross-org initiative for [Efficient AI at Scale]. Our focus is on efficient learning of massive neural networks for both model (e.g., neural architecture search, model compression, sparse and modular learning) and data efficiency (e.g., zero-shot and few-shot learning, semi-supervised learning). We develop state-of-the-art computationally efficient models and techniques to enable AI practitioners, researchers and engineers to use large-scale models in practice. Our technologies have been deployed in several enterprise scenarios including Turing, Bing and Microsoft 365. Honors: 2022 MIT Technology Review Innovators under 35 Semi-finalist (listed in 100 innovators under 35 world-wide) for work on Efficient AI.

Guanchu Wang (Rice University)
Ruixiang Tang (Texas A&M University)

I am passionate about using AI and Machine Learning to create intelligent user experiences that connect people to information. I lead a research and incubation team in Microsoft Research Technologies. Our work at the Language and Information Technologies team is focused on creating language understanding and user modeling technologies to enable intelligent experiences in multiple products. Our work has been shipped in several products such as Bing, Cortana, Office 365, and Dynamics 365. I have hands-on experience building and shipping state-of-the-art ML/AI algorithms. I also have experience building and managing world-class teams of scientists and engineers. My research interests are at the intersection of machine learning, language understanding, and information retrieval. A key part of my work involves using Machine Learning to model large-scale text and user behavior data with applications to intelligent assistants, search, user modeling, quality evaluation, recommendation and personalization. I received my Ph.D. from the department of Computer Science and Engineering at the University of Michigan Ann Arbor. I Invented, published, and patented new approaches in language understanding, information retrieval and machine learning. I published 60+ peer-reviewed papers in these areas and I am an inventor on 20+ (granted and pending) patents.

Xia Hu (Texas A&M University)

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