Title

Algorithm Appreciation: Algorithmic Performance, Developmental Processes, and User Interactions

Source of Publication

Proceedings of the 2020 IEEE International Conference on Communications, Computing, Cybersecurity, and Informatics, CCCI 2020

Abstract

© 2020 IEEE. In this research, we conduct an online experiment to better understand perceptions of algorithmic features (fairness, accountability, transparency, and explainability) This study identifies key factors of algorithm and conceptualizes such issues in relation to trust by testing how they affect user emotion and satisfaction of personalized machine learning algorithms. The results indicate the heuristic roles of algorithmic characteristics in terms of their underlying links to trust and subsequent behaviors. Users experience a dual-process in assessing AI features and formulating trust through their heuristic-systematic evaluations. Heuristic and systematic processes are positively linked to trust and systematic processes are positively connected to trust and performance expectancy, which serve as antecedents of emotions. Heuristic and systematic processes offer a useful perspective on the conceptualization of AI experience and interaction. User cognitive processes identified provide solid foundations for algorithm design and development and a stronger basis for the design of sensemaking AI services.

Document Type

Conference Proceeding

ISBN

9781728120355

Publisher

IEEE

Publication Date

11-3-2020

DOI

10.1109/ccci49893.2020.9256470

Scopus ID

85097846209

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