Mixed-Initiative Interface Personalization as a Case Study in Usable AI

Authors

  • Andrea Bunt University of Manitoba
  • Cristina Conati University of British Columbia
  • Joanna McGrenere University of British Columbia

DOI:

https://doi.org/10.1609/aimag.v30i4.2264

Keywords:

interface personalization, evaluation

Abstract

Interface personalization aims to streamline the process of working in a feature-rich application by providing the user with an adapted interface tailored specifically to his/her needs. The MICA (Mixed-Initiative Customization Assistance) system explores a middle ground between two opposing approaches to personalization: (1) an adaptable approach, where personalization is fully user controlled and (2) and adaptive approach, where personalization is fully system controlled. We overview MICA’s strategy for providing user-adaptive recommendations to help users decide how to personalize their interfaces. In doing so, we focus primarily on how MICA handles threats to usability that are often found in adaptive interfaces including obtrusiveness and lack of understandability and control. We also describe how we evaluated MICA and highlight results from these evaluations.

Author Biographies

Andrea Bunt, University of Manitoba

Assistant ProfessorDepartment of Computer Science

Cristina Conati, University of British Columbia

Associate ProfessorDepartment of Computer Science

Joanna McGrenere, University of British Columbia

Associate ProfessorDepartment of Computer Science

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Published

2010-01-02

How to Cite

Bunt, A., Conati, C., & McGrenere, J. (2010). Mixed-Initiative Interface Personalization as a Case Study in Usable AI. AI Magazine, 30(4), 58. https://doi.org/10.1609/aimag.v30i4.2264

Issue

Section

Articles