TY  - CPAPER
U1  - Konferenzveröffentlichung
A1  - Chau, Polo
A1  - Endert, Alex
A1  - Keim, Daniel A.
A1  - Oelke, Daniela
T1  - Interactive Visualization for Fostering Trust in ML
T2  - Dagstuhl Reports
N2  - The use of artificial intelligence continues to impact a broad variety of domains, application areas, and people. However, interpretability, understandability, responsibility, accountability, and fairness of the algorithms' results - all crucial for increasing humans' trust into the systems - are still largely missing. The purpose of this seminar is to understand how these components factor into the holistic view of trust. Further, this seminar seeks to identify design guidelines and best practices for how to build interactive visualization systems to calibrate trust.
KW  - accountability
KW  - artificial intelligence
KW  - explainability
KW  - fairness
KW  - interactive visualization
KW  - machine learning
KW  - responsibility
KW  - trust
KW  - understandability
Y1  - 2023
SN  - 2192-5283
SS  - 2192-5283
U6  - https://doi.org/10.4230/DagRep.12.8.103
DO  - https://doi.org/10.4230/DagRep.12.8.103
VL  - 12
IS  - 8
SP  - 103
EP  - 116
PB  - Schloss-Dagstuhl - Leibniz Zentrum für Informatik
ER  -