Name
Title | ||
|---|---|---|
The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI |
Abstract | ||
|---|---|---|
•This study examines the effect of explainability in AI on user trust and attitudes toward AI.•Conceptualizes causability as an antecedent of explainability and as a key cue of an algorithm and examines them in relation to trust.•The dual roles of causability and explainability in terms of its underlying links to trust.•Causability lends the justification for what and how should be explained.•Causable explainable AI will help people understand the decision-making process of AI algorithms. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.ijhcs.2020.102551 | International Journal of Human-Computer Studies |
Keywords | DocType | Volume |
Explainable Ai,Causability,Human-ai interaction,Explanatorycues,Interpretability,Understandability,Trust,Glassbox,Human-centeredAI | Journal | 146 |
ISSN | Citations | PageRank |
1071-5819 | 7 | 0.55 |
References | Authors | |
0 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong-Hee Shin | 1 | 894 | 48.01 |