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Found 5 papers, 2 papers with code
Invisible Users: Uncovering End-Users' Requirements for Explainable AI via Explanation Forms and Goals
The EUCA study findings, the identified explanation forms and goals for technical specification, and the EUCA study dataset support the design and evaluation of end-user-centered XAI techniques for accessible, safe, and accountable AI.
Transcending XAI Algorithm Boundaries through End-User-Inspired Design
The boundaries of existing explainable artificial intelligence (XAI) algorithms are confined to problems grounded in technical users' demand for explainability.
EUCA: the End-User-Centered Explainable AI Framework
The ability to explain decisions to end-users is a necessity to deploy AI as critical decision support.
Decision Making
Explainable artificial intelligence
Human-Computer Interaction
A Comparative Study of Western and Chinese Classical Music based on Soundscape Models
In this study, we examine whether we can analyze and compare Western and Chinese classical music based on soundscape models.
Multi-label Sound Event Retrieval Using a Deep Learning-based Siamese Structure with a Pairwise Presence Matrix
State of the art sound event retrieval models have focused on single-label audio recordings, with only one sound event occurring, rather than on multi-label audio recordings (i. e., multiple sound events occur in one recording).
