1 code implementation • 10 Feb 2023 • Weina Jin, Jianyu Fan, Diane Gromala, Philippe Pasquier, Ghassan Hamarneh
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.
no code implementations • 18 Aug 2022 • Weina Jin, Jianyu Fan, Diane Gromala, Philippe Pasquier, Xiaoxiao Li, Ghassan Hamarneh
The boundaries of existing explainable artificial intelligence (XAI) algorithms are confined to problems grounded in technical users' demand for explainability.
1 code implementation • 4 Feb 2021 • Weina Jin, Jianyu Fan, Diane Gromala, Philippe Pasquier, Ghassan Hamarneh
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
no code implementations • 20 Feb 2020 • Jianyu Fan, Yi-Hsuan Yang, Kui Dong, Philippe Pasquier
In this study, we examine whether we can analyze and compare Western and Chinese classical music based on soundscape models.
no code implementations • 20 Feb 2020 • Jianyu Fan, Eric Nichols, Daniel Tompkins, Ana Elisa Mendez Mendez, Benjamin Elizalde, Philippe Pasquier
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).