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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 7: AAAI-21 Technical Tracks 7

Improving the Performance-Compatibility Tradeoff with Personalized Objective Functions

February 1, 2023

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Authors

Jonathan Martinez

Ben-Gurion University


Kobi Gal

Ben-Gurion University, University of Edinburgh


Ece Kamar

Microsoft Research


Levi H. S. Lelis

University of Alberta Alberta Machine Intelligence Institute (Amii)


DOI:

10.1609/aaai.v35i7.16745


Abstract:

AI-systems that model and interact with their users can up-date 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.

Topics: AAAI

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HOW TO CITE:

Jonathan Martinez||Kobi Gal||Ece Kamar||Levi H. S. Lelis Improving the Performance-Compatibility Tradeoff with Personalized Objective Functions Proceedings of the AAAI Conference on Artificial Intelligence (2021) 5967-5974.

Jonathan Martinez||Kobi Gal||Ece Kamar||Levi H. S. Lelis Improving the Performance-Compatibility Tradeoff with Personalized Objective Functions AAAI 2021, 5967-5974.

Jonathan Martinez||Kobi Gal||Ece Kamar||Levi H. S. Lelis (2021). Improving the Performance-Compatibility Tradeoff with Personalized Objective Functions. Proceedings of the AAAI Conference on Artificial Intelligence, 5967-5974.

Jonathan Martinez||Kobi Gal||Ece Kamar||Levi H. S. Lelis. Improving the Performance-Compatibility Tradeoff with Personalized Objective Functions. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.5967-5974.

Jonathan Martinez||Kobi Gal||Ece Kamar||Levi H. S. Lelis. 2021. Improving the Performance-Compatibility Tradeoff with Personalized Objective Functions. "Proceedings of the AAAI Conference on Artificial Intelligence". 5967-5974.

Jonathan Martinez||Kobi Gal||Ece Kamar||Levi H. S. Lelis. (2021) "Improving the Performance-Compatibility Tradeoff with Personalized Objective Functions", Proceedings of the AAAI Conference on Artificial Intelligence, p.5967-5974

Jonathan Martinez||Kobi Gal||Ece Kamar||Levi H. S. Lelis, "Improving the Performance-Compatibility Tradeoff with Personalized Objective Functions", AAAI, p.5967-5974, 2021.

Jonathan Martinez||Kobi Gal||Ece Kamar||Levi H. S. Lelis. "Improving the Performance-Compatibility Tradeoff with Personalized Objective Functions". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.5967-5974.

Jonathan Martinez||Kobi Gal||Ece Kamar||Levi H. S. Lelis. "Improving the Performance-Compatibility Tradeoff with Personalized Objective Functions". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 5967-5974.

Jonathan Martinez||Kobi Gal||Ece Kamar||Levi H. S. Lelis. Improving the Performance-Compatibility Tradeoff with Personalized Objective Functions. AAAI[Internet]. 2021[cited 2023]; 5967-5974.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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