Events Calendar

PhD Thesis Proposal - Emily Sheng

Title: Towards Fairness in Natural Language Processing

Date/Time: Tuesday, March 3rd, 10-11:30am
Location: SAL 213

Candidate: Emily Sheng

Committee: Prof. Prem Natarajan (advisor), Prof. Nanyun Peng, Prof. Aram Galstyan, Prof. Shri Narayanan, Prof. Yan Liu

Abstract: With the advent of more effective, large-scale natural language processing (NLP) techniques, issues of fairness and bias in NLP techniques have become increasingly important. Biased models have the potential to perpetuate and amplify societal biases, which has implications for ethics, model robustness, and model interpretability. First, we describe our work to define biases in a language generation setting. We subsequently describe how different definitions of bias can be used to analyze biases in existing NLP systems, e.g., language generation and named entity recognition. Finally, we propose techniques that allow us to move towards the mitigation and control of biases. This talk will examine the importance of defining tasks and metrics for biases in NLP, how our bias analyses can inform our approach to bias mitigation, and related directions in how we can move towards fairness in NLP.

Location: Henry Salvatori Computer Science Center (SAL) - 213

Audiences: Everyone Is Invited

Contact: Lizsl De Leon