Trust Indicators and Explainable AI: A Study on User Perceptions - IFIP - Lecture Notes in Computer Science Access content directly
Conference Papers Year : 2021

Trust Indicators and Explainable AI: A Study on User Perceptions

Abstract

Nowadays, search engines, social media or news aggregators are the preferred services for news access. Aggregation is mostly based on artificial intelligence technologies raising a new challenge: Trust has been ranked as the most important factor for media business. This paper reports findings of a study evaluating the influence of manipulations of interface design and information provided in the context of eXplainable Artificial Intelligence (XAI) on user perception and in the context of news content aggregators. In an experimental online study, various layouts and scenarios have been developed, implemented and tested with 266 participants. Measures of trust, understanding and preference were recorded. Results showed no influence of the factors on trust. However, data indicates that the influence of the layout, for example implicit integration of media source through layout structuration has a significant effect on perceived importance to cite the source of a media. Moreover, the amount of information presented to explain the AI showed a negative influence on user understanding. This highlights the importance and difficulty of making XAI understandable for its users.
Fichier principal
Vignette du fichier
520516_1_En_39_Chapter.pdf (422.06 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04196849 , version 1 (05-09-2023)

Licence

Attribution

Identifiers

Cite

Delphine Ribes, Nicolas Henchoz, Hélène Portier, Lara Defayes, Thanh-Trung Phan, et al.. Trust Indicators and Explainable AI: A Study on User Perceptions. 18th IFIP Conference on Human-Computer Interaction (INTERACT), Aug 2021, Bari, Italy. pp.662-671, ⟨10.1007/978-3-030-85616-8_39⟩. ⟨hal-04196849⟩
8 View
2 Download

Altmetric

Share

Gmail Facebook X LinkedIn More