AI Evaluation

This research area focuses on core methods for evaluating AI systems, including development of quantitative metrics, mixed methods approaches that combine qualitative with quantitative insights, and contextual meta-evaluation methods.

Publications

A. Olteanu, S.-L. Blodgett, A. Balayn, A. Wang, F. Diaz, F. du Pin Calmon, M. Mitchell, M. D. Ekstrand, R. Binns, S. Barocas
arxiv, 2025
A. Deviyani, F. Diaz
NAACL 2025
N. Arabzadeh, F. Diaz, J. He
SIGIR-AP 2024
M. D. Ekstrand, B. Carterette, F. Diaz
ACM Transactions on Recommender Systems, March 2024
F. Diaz
Perspectives on the Evaluation of Recommender Systems Workshop, RecSys 2021
P. Chandar, F. Diaz, C. Hosey, B. St. Thomas
KDD 2021
O. Kirnap, F. Diaz, A. J. Biega, M. Ekstrand, B. Carterette, E. Yilmaz
WWW 2021
P. Chandar, F. Diaz, B. St. Thomas
NeurIPS 2020
F. Diaz, B. Mitra, M. D. Ekstrand, A. J. Biega, B. Carterette
CIKM 2020
J. Garcia-Gathright, C. Hosey, B. St. Thomas, B. Carterette, F. Diaz
RecSys 2018
J. Garcia-Gathright, B. St. Thomas, C. Hosey, Z. Nazari, F. Diaz
SIGIR 2018
R. Mehrotra, A. Anderson, F. Diaz, A. Sharma, H. Wallach, E. Yilmaz
WWW 2017
M. Ekstrand, R. McCreadie, V. Pavlu, F. Diaz
CIKM 2016
F. Diaz, M. Gamon, J. Hofman, E. Kiciman, D. Rothschild
PLoS ONE, January 2016
P. Golbus, I. Zitouni, J. Kim, A. Hassan, F. Diaz
WWW 2014