TY - JOUR
T1 - Challenges in Evaluating Interactive Visual Machine Learning Systems
AU - Boukhelifa, N.
AU - Bezerianos, A.
AU - Chang, R.
AU - Collins, C.
AU - Drucker, S.
AU - Endert, A.
AU - Hullman, J.
AU - North, C.
AU - Sedlmair, M.
N1 - Publisher Copyright:
© 1981-2012 IEEE.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - In interactive visual machine learning (IVML), humans and machine learning algorithms collaborate to achieve tasks mediated by interactive visual interfaces. This human-in-the-loop approach to machine learning brings forth not only numerous intelligibility, trust, and usability issues, but also many open questions with respect to the evaluation of the IVML system, both as separate components, and as a holistic entity that includes both human and machine intelligence. This article describes the challenges and research gaps identified in an IEEE VIS workshop on the evaluation of IVML systems.
AB - In interactive visual machine learning (IVML), humans and machine learning algorithms collaborate to achieve tasks mediated by interactive visual interfaces. This human-in-the-loop approach to machine learning brings forth not only numerous intelligibility, trust, and usability issues, but also many open questions with respect to the evaluation of the IVML system, both as separate components, and as a holistic entity that includes both human and machine intelligence. This article describes the challenges and research gaps identified in an IEEE VIS workshop on the evaluation of IVML systems.
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U2 - 10.1109/MCG.2020.3017064
DO - 10.1109/MCG.2020.3017064
M3 - Article
C2 - 33095702
AN - SCOPUS:85094685122
SN - 0272-1716
VL - 40
SP - 88
EP - 96
JO - IEEE Computer Graphics and Applications
JF - IEEE Computer Graphics and Applications
IS - 6
M1 - 9238590
ER -