Improving the Performance-Compatibility Tradeoff with Personalized Objective Functions

Jonathan Martinez, Kobi Gal, Ece Kamar, Levi H.S. Lelis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

AI-systems that model and interact with their users can update their models over time to reflect new information and changes in the environment. Although these updates may improve the overall performance of the AI-system, they may actually hurt the performance with respect to individual users. Prior work has studied the tradeoff between improving the system’s performance following an update and the compatibility of the updated system with prior user experience. The more the model is forced to be compatible with a prior version, the higher loss in performance it will incur. This paper challenges this assumption by showing that by personalizing the loss function to specific users, it is possible to increase the prediction performance of the AI-system while sacrificing less compatibility for these users. Our approach updates the sample weights to reflect their contribution to the compatibility of the model for a particular user following the update. We construct a portfolio of different models that vary in how they personalize the loss function for a user. We select the best model to use for a target user based on a validation set. We apply this approach to three supervised learning tasks commonly used in the human-computer decision-making literature. We show that using our approach leads to significant improvements in the performance-compatibility tradeoff over the non-personalized approach of Bansal et al., achieving up to 300% improvement for certain users. We present several use cases that illustrate the difference between the personalized and non-personalized approach for two of our domains.

Original languageAmerican English
Title of host publication35th AAAI Conference on Artificial Intelligence, AAAI 2021
Pages5967-5974
Number of pages8
ISBN (Electronic)9781713835974
StatePublished - 1 Jan 2021
Event35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online
Duration: 2 Feb 20219 Feb 2021

Publication series

Name35th AAAI Conference on Artificial Intelligence, AAAI 2021
Volume7

Conference

Conference35th AAAI Conference on Artificial Intelligence, AAAI 2021
CityVirtual, Online
Period2/02/219/02/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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