TY  - JOUR
A1  - Abdel‑Karim, Benjamin M.
A1  - Pfeuffer, Nicolas
A1  - Rohde, Gernot Gerhard Ulrich
A1  - Hinz, Oliver
T1  - How and what can humans learn from being in the loop? : Invoking contradiction learning as a measure to make humans smarter
T2  - KI - Künstliche Intelligenz
N2  - This article discusses the counterpart of interactive machine learning, i.e., human learning while being in the loop in a human-machine collaboration. For such cases we propose the use of a Contradiction Matrix to assess the overlap and the contradictions of human and machine predictions. We show in a small-scaled user study with experts in the area of pneumology (1) that machine-learning based systems can classify X-rays with respect to diseases with a meaningful accuracy, (2) humans partly use contradictions to reconsider their initial diagnosis, and (3) that this leads to a higher overlap between human and machine diagnoses at the end of the collaboration situation. We argue that disclosure of information on diagnosis uncertainty can be beneficial to make the human expert reconsider her or his initial assessment which may ultimately result in a deliberate agreement. In the light of the observations from our project, it becomes apparent that collaborative learning in such a human-in-the-loop scenario could lead to mutual benefits for both human learning and interactive machine learning. Bearing the differences in reasoning and learning processes of humans and intelligent systems in mind, we argue that interdisciplinary research teams have the best chances at tackling this undertaking and generating valuable insights.
KW  - Machine teaching
KW  - Machine learning
KW  - Experts
KW  - Feedback loop
Y1  - 2020
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/55592
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-555920
SN  - 1610-1987
SN  - 0933-1875
VL  - 34
SP  - 199
EP  - 207
PB  - Springer
CY  - Berlin ; Heidelberg
ER  -