October 11, 2019
TransFAT: Translating Fairness, Accountability, and Transparency into Data Science Practice
Abstract: Data science technology promises to improve people’s lives, accelerate scientific discovery and innovation, and bring about positive societal change. Yet, if not used responsibly, this same technology can reinforce inequity, limit accountability, and infringe on the privacy of individuals. In my talk I will discuss recent technical work in scope of the “Data, Responsibly” project. The goal of this project is to establish a foundational new role for database technology, in which managing data in accordance with ethical and moral norms, and legal and policy considerations becomes a core system requirement. I will connect our technical insights on fairness, diversity, transparency, and data protection to ongoing regulatory efforts in the US and elsewhere. Additional information about the project is available at https://dataresponsibly.
Julia Stoyanovich is an Assistant Professor at New York University in the Department of Computer Science and Engineering at the Tandon School of Engineering, and the Center for Data Science. Julia’s research focuses on responsible data management and analysis practices: on operationalizing fairness, diversity, transparency, and data protection in all stages of the data acquisition and processing lifecycle. She established the Data, Responsibly consortium (https://dataresponsibly.