Research paper published in IJIM
Together with colleagues from JMU Würzburg, we published a paper on the trade-off between explainability and accuracy in ML research.
Explaining #AI system decision models to users is becoming ever more important. But mathematical and programmatic considerations do not suffice to scrutinize applications with humans.
We show that we should neither simplify the tradeoff between performance and explainability as continuous nor that the data-driven interpretability of algorithms entails algorithm explainability towards end users. Rather, we show that there are currently three groups of algorithm explainability somewhat distinct in performance capabilities. Hence, we say: Stop Ordering Machine Learning Algorithms by their Explainability.
The article is available here and open access in the International Journal of Information Management, #1 journal in SJR for Management Information Systems & Information Systems and Management.