Investigating fairness in machine learning with Na Zou

Based on data, machine learning can quickly and efficiently analyze large amounts of information to provide suggestions and help make decisions. For example, phones and computers expose us to machine learning technologies such as voice recognition, personalized shopping suggestions, targeted advertisements and email filtering. However, it also brings challenges related to bias in the data and algorithms it uses, potentially leading to discrimination against specific individuals or groups. To help combat this problem, Dr Na Zou, an assistant professor in the Department of Engineering Technology and Industrial Distribution at Texas A&M University, aims to develop a data-centric fairness framework. To support her research, Zou received the National Science Foundation's Faculty Early Career Development Program (CAREER) Award.