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Poster
When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction
Vinith Suriyakumar · Marzyeh Ghassemi · Berk Ustun

Thu Jul 27 04:30 PM -- 06:00 PM (PDT) @ Exhibit Hall 1 #209
in Poster Session 6 »

Machine learning models are often personalized with categorical attributes that define groups. In this work, we show that personalization with group attributes can inadvertently reduce performance at a group level -- i.e., groups may receive unnecessarily inaccurate predictions by sharing their personal characteristics. We present formal conditions to ensure the fair use of group attributes in a prediction task, and describe how they can be checked by training one additional model. We characterize how fair use conditions be violated due to standard practices in model development, and study the prevalence of fair use violations in clinical prediction tasks. Our results show that personalization often fails to produce a tailored performance gain for every group who reports personal data, and underscore the need to evaluate fair use when personalizing models with characteristics that are protected, sensitive, self-reported, or costly to acquire.

Author Information

Vinith Suriyakumar (Massachusetts Institute of Technology)
Marzyeh Ghassemi (MIT)
Marzyeh Ghassemi

Dr. Marzyeh Ghassemi is an Assistant Professor at MIT in Electrical Engineering and Computer Science (EECS) and Institute for Medical Engineering & Science (IMES), and a Vector Institute faculty member holding a Canadian CIFAR AI Chair and Canada Research Chair. She holds MIT affiliations with the Jameel Clinic and CSAIL. Professor Ghassemi holds a Herman L. F. von Helmholtz Career Development Professorship, and was named a CIFAR Azrieli Global Scholar and one of MIT Tech Review’s 35 Innovators Under 35. Previously, she was a Visiting Researcher with Alphabet’s Verily. She is currently on leave from the University of Toronto Departments of Computer Science and Medicine. Prior to her PhD in Computer Science at MIT, she received an MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar, and B.S. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University.

Berk Ustun (UCSD)

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