Publication
Explanatory Interactive Machine Learning
Nicolas Pfeuffer; Lorenz Baum; Wolfgang Stammer; Benjamin M. Abdel-Karim; Patrick Schramowski; Andreas M. Bucher; Christian Hügel; Gernot Rohde; Kristian Kersting; Oliver Hinz
In: Business & Information Systems Engineering (BISE), Vol. 65, No. 6, Pages 677-701, Springer Nature, 2023.
Abstract
The most promising standard machine learning
methods can deliver highly accurate classification results,
often outperforming standard white-box methods. How-
ever, it is hardly possible for humans to fully understand
the rationale behind the black-box results, and thus, these
powerful methods hamper the creation of new knowledge
on the part of humans and the broader acceptance of this
technology. Explainable Artificial Intelligence attempts to
overcome this problem by making the results more inter-
pretable, while Interactive Machine Learning integrates
humans into the process of insight discovery. The paper
builds on recent successes in combining these two cutting-
edge technologies and proposes how Explanatory Interac-
tive Machine Learning (XIL) is embedded in a generaliz-
able Action Design Research (ADR) process – called XIL-
ADR. This approach can be used to analyze data, inspect
models, and iteratively improve them. The paper shows the
application of this process using the diagnosis of viral
pneumonia, e.g., Covid-19, as an illustrative example. By
these means, the paper also illustrates how XIL-ADR can
help identify shortcomings of standard machine learning
projects, gain new insights on the part of the human user,
and thereby can help to unlock the full potential of AI-
based systems for organizations and research.