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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) @ None #None

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)

I am a senior scientist at Microsoft Research (MSR) working at the intersection of natural language understanding, deep learning and transfer learning. My current research is focused on making AI accessible to all with two major themes: (1) Scaling deep and large-scale natural language understanding models to scenarios with limited computational resources leveraging techniques like self-supervised, weakly supervised and curriculum learning, data augmentation, knowledge distillation, etc. (2) Building trustworthy AI for mitigating misinformation and bias to provide fair and equitable information access to all. Prior to joining MSR, I was leading the information extraction efforts to build the Amazon Product Knowledge Graph, an authoritative knowledge graph for all products in the world. I graduated summa cum laude from the Max Planck Institute for Informatics, Germany with a PhD in 2017. I was awarded the 2018 SIGKDD Doctoral Dissertation Runner-up Award for my thesis on credibility analysis and misinformation.

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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