TY - JOUR
T1 - What should be in an XAI explanation? What IFT reveals
AU - Dodge, Jonathan
AU - Penney, Sean
AU - Anderson, Andrew
AU - Burnett, Margaret
N1 - Funding Information:
This work was supported by DARPA #N66001-17-2-4030 and NSF #1314384. Any opinions, findings and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of NSF, DARPA, the Army Research Office, or the US government.
Publisher Copyright:
© 2018 Copyright for the individual papers remains with the authors.
PY - 2018
Y1 - 2018
N2 - This workshop's call for participation poses the question: What should be in an explanation? One route toward answering this question is to turn to theories of how humans try to obtain information they seek. Information Foraging Theory (IFT) is one such theory. In this paper, we present lessons we have learned about how IFT informs Explainable Artificial Intelligence (XAI), and also what XAI contributes back to IFT.
AB - This workshop's call for participation poses the question: What should be in an explanation? One route toward answering this question is to turn to theories of how humans try to obtain information they seek. Information Foraging Theory (IFT) is one such theory. In this paper, we present lessons we have learned about how IFT informs Explainable Artificial Intelligence (XAI), and also what XAI contributes back to IFT.
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M3 - Conference article
AN - SCOPUS:85044536977
SN - 1613-0073
VL - 2068
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 2018 Joint ACM IUI Workshops, ACMIUI-WS 2018
Y2 - 11 March 2018
ER -