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I. Tiddi and S. Schlobach

Artiﬁcial Intelligence 302 (2022) 103627

Understanding the human role An  open  challenge  remains  to  understand  the  role  of  humans  in  KBX-systems,  i.e.  if  and 
how much they should be involved in the process of generating explanation. Some KBX-systems have suggested that better 
performances are achieved when human users provide feedback to the system. In this sense, an idea to be investigated is the 
applicability of reinforcement learning and humans-in-the-loop in KBX-systems (so far unexplored), integrating principles 
and  methodologies  of  hybrid  intelligence  and  collaborative  AI [104].  Human  assessment  should  also  be  employed  in  the 
development of benchmarks for KBX-systems, currently lacking in the ﬁeld, to allow a better understanding of what is a 
“good explanation” from a human perspective. This would also support a better characterisation of useful explanations in 
terms of types and satisfaction criteria.
Knowledge graphs as tools for explainable machine learning: A survey