(Submitted on 5 Apr 2020)

Abstract: AI systems that model and interact with users can update their models over
time to reflect new information and changes in the environment. Although these
updates can improve the performance of the AI system, they may actually hurt
the performance for individual users. Prior work has studied the trade-off
between improving the system accuracy following an update and the compatibility
of the update with prior user experience. The more the model is forced to be
compatible with prior updates, the higher loss in accuracy it will incur. In
this paper, we show that in some cases it is possible to improve this
compatibility-accuracy trade-off relative to a specific user by employing new
error functions for the AI updates that personalize the weight updates to be
compatible with the user’s history of interaction with the system and present
experimental results indicating that this approach provides major improvements
to certain users.