CMU Artificial Intelligence Seminar Series sponsored by
Tuesday, Nov 16, 2021
Time: 12:00 - 01:00 PM ET
Moritz Hardt -- Retiring Adult: New Datasets for Fair Machine Learning
Relevant Paper(s):
Abstract: Although the fairness community has recognized the importance of data,
researchers in the area primarily rely on UCI Adult when it comes to
tabular data. Derived from a 1994 US Census survey, this dataset has
appeared in hundreds of research papers where it served as the basis
for the development and comparison of many algorithmic fairness
interventions. We reconstruct a superset of the UCI Adult data from
available US Census sources and reveal idiosyncrasies of the UCI Adult
dataset that limit its external validity. Our primary contribution is
a suite of new datasets derived from US Census surveys that extend the
existing data ecosystem for research on fair machine learning. We
create prediction tasks relating to income, employment, health,
transportation, and housing. The data span multiple years and all
states of the United States, allowing researchers to study temporal
shift and geographic variation. We highlight a broad initial sweep of
new empirical insights relating to trade-offs between fairness
criteria, performance of algorithmic interventions, and the role of
distribution shift based on our new datasets. Our findings inform
ongoing debates, challenge some existing narratives, and point to
future research directions. Our datasets are available at
folktables.org. Joint work with Frances Ding, John
Miller, and Ludwig Schmidt.
Bio: Moritz Hardt is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His work builds foundations of machine learning and algorithmic decision making with a focus on social context, interaction, and impact. Hardt obtained a PhD in Computer Science from Princeton University with a dissertation on privacy-preserving data analysis and fairness in classification. He then held research positions at IBM Research and Google. Hardt co-founded the Workshop on Fairness, Accountability, and Transparency in Machine Learning. He is a co-author of "Fairness and Machine Learning: Limitations and Opportunities" and "Patterns, Predictions, and Actions: A Story about Machine Learning". He has received an NSF CAREER award, a Sloan fellowship, and best paper awards at ICML 2018 and ICLR 2017.