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
© 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.
Social and Behavioral Sciences
algorithmic features, artificial intelligence, explainability, human-machine learning interaction, trust
Shin, Donghee; Zaid, Bouziane; and Ibahrine, Mohammed, "Algorithm Appreciation: Algorithmic Performance, Developmental Processes, and User Interactions" (2020). All Works. 393.
Indexed in Scopus