Applied Data Science Invited Speakers

The Applied Data Science Invited Talks will provide a venue for leading experts in the world of applied data mining and knowledge discovery. These invited talks will feature highly influential speakers who have directly contributed to successful data mining applications in their respective fields. The talks and discussions will focus on innovative and leading-edge, large-scale industry or government applications of data mining in areas such as finance, health-care, bio-informatics, public policy, infrastructure, telecommunications, social media and computational advertising.

Keynote: Timnit Gebru

Timnit Gebru


Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning

A growing body of work shows that many problems in fairness, accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process. We argue that a new specialization should be formed within machine learning that is focused on methodologies for data collection and annotation: efforts that require institutional frameworks and procedures. Specifically for sociocultural data, parallels can be drawn from archives and libraries. Archives are the longest standing communal effort to gather human information and archive scholars have already developed the language and procedures to address and discuss many challenges pertaining to data collection such as consent, power, inclusivity, transparency, and ethics privacy. We discuss these five key approaches in document collection practices in archives that can inform data collection in sociocultural machine learning.

Timnit Gebru is currently a research scientist at Google in the ethical AI team. Prior to that she did a postdoc at Microsoft Research, New York City in the FATE (Fairness Transparency Accountability and Ethics in AI) group, where she studied algorithmic bias and the ethical implications underlying any data mining project (see this New York Times article for an example of my work). She received her PhD from the Stanford Artificial Intelligence Laboratory, studying computer vision under Fei-Fei Li. Her thesis pertains to data mining large scale publicly available images to gain sociological insight, and working on computer vision problems that arise as a result. The Economist, The New York Times and others have covered part of this work. Some of the computer vision areas she is interested in include fine-grained image recognition, scalable annotation of images, and domain adaptation. Prior to joining Fei-Fei's lab she worked at Apple designing circuits and signal processing algorithms for various Apple products including the first iPad. She also spent an obligatory year as an entrepreneur (as all Stanford undergrads seem to do). Her research was supported by the NSF foundation GRFP fellowship and the Stanford DARE fellowship.