Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning

Sebastian Robert, Sebastian Büttner, Carsten Röcker, Andreas Holzinger

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


In this paper, we present the current state-of-the-art of decision making (DM) and machine learning (ML) and bridge the two research domains to create an integrated approach of complex problem solving based on human and computational agents. We present a novel classification of ML, emphasizing the human-in-the-loop in interactive ML (iML) and more specific on collaborative interactive ML (ciML), which we understand as a deep integrated version of iML, where humans and algorithms work hand in hand to solve complex problems. Both humans and computers have specific strengths and weaknesses and integrating humans into machine learning processes might be a very efficient way for tackling problems. This approach bears immense research potential for various domains, e.g., in health informatics or in industrial applications. We outline open questions and name future challenges that have to be addressed by the research community to enable the use of collaborative interactive machine learning for problem solving in a large scale.
Original languageEnglish
Title of host publicationMachine Learning for Health Informatics
Publication statusPublished - 8 Apr 2017

Publication series

NameLecture Notes in Computer Science
VolumeLNAI 9605
ISSN (Electronic)1611-3349


  • Machine Learning

ASJC Scopus subject areas

  • Artificial Intelligence

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)


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