How Does Predicate Invention Affect Human Comprehensibility?

By Ute Schmid, Christina Zeller, Tarek R. Besold, Alireza Tamaddoni-Nezhad, Stephen H. Muggleton. 2016

44 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

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Citations in the corpus

What Does Explainable AI Really Mean? A New Conceptualization of Perspectives (2017, background): “Combining their results with work investigating factors inﬂuencing the human comprehensibility of representation formats and reasoning approaches [13] might pave the way towards systems eﬀectively providing full explanations of their own to their users.”
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation (2018, background): “Schmid et al. [2016] ﬁnd that human-recognizable intermediate predicates in inductive knowledge programs can sometimes improve simulation time.”
Human Evaluation of Models Built for Interpretability (2019, background): “Schmid et al. (2016) ﬁnd that human-recognizable intermediate predicates in inductive knowledge programs can some-times improve simulation time.”
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning (2019, background): “…human-computer interaction (HCI) researchers actively explore this area ( 10,44–46 ), and propose several metrics for human evaluation on general explanations from IML, such as mental mode ( 15, 47–50 ), human-machine performance ( 51–55 ), user satisfaction ( 9, 56–58 ) and user trust ( 59–61 ).”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, unknown): “Bilgic et al. 2005[19] ◆ ◆ ◆ Bunt et al. 2012 [25] ◆ ◆ Gedikli et al. 2014 [62] ◆ ◆ Kulesza et al. 2013 [111] ◆ ◆ ◆ ◆ Lim et al. 2009 [125] ◆ ◆ ◆ ◆ ◆ ◆ Lage et al. 2019 [112] ◆ ◆ ◆ Schmid et al. 2016 [186] ◆ ◆ Berkovsky et al. 2017 [17] ◆ ◆ ◆ Glass et al. 2008 [65] ◆ ◆ ◆ Haynes et al. 2009 [74] ◆ ◆ ”
Accuracy and Interpretability Trade-Offs in Machine Learning Applied to Safer Gambling (2016, background): “Also, the congruence of decision tests with familiar concepts is likely to influence interpretability [11] and the usefulness for practitioners such as therapists or in automatically generated personalised messages to the user.”