The role of causality in explainable artificial intelligence
- URL: http://arxiv.org/abs/2309.09901v1
- Date: Mon, 18 Sep 2023 16:05:07 GMT
- Title: The role of causality in explainable artificial intelligence
- Authors: Gianluca Carloni, Andrea Berti, Sara Colantonio
- Abstract summary: Causality and eXplainable Artificial Intelligence (XAI) have developed as separate fields in computer science.
We investigate the literature to try to understand how and to what extent causality and XAI are intertwined.
- Score: 1.049712834719005
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Causality and eXplainable Artificial Intelligence (XAI) have developed as
separate fields in computer science, even though the underlying concepts of
causation and explanation share common ancient roots. This is further enforced
by the lack of review works jointly covering these two fields. In this paper,
we investigate the literature to try to understand how and to what extent
causality and XAI are intertwined. More precisely, we seek to uncover what
kinds of relationships exist between the two concepts and how one can benefit
from them, for instance, in building trust in AI systems. As a result, three
main perspectives are identified. In the first one, the lack of causality is
seen as one of the major limitations of current AI and XAI approaches, and the
"optimal" form of explanations is investigated. The second is a pragmatic
perspective and considers XAI as a tool to foster scientific exploration for
causal inquiry, via the identification of pursue-worthy experimental
manipulations. Finally, the third perspective supports the idea that causality
is propaedeutic to XAI in three possible manners: exploiting concepts borrowed
from causality to support or improve XAI, utilizing counterfactuals for
explainability, and considering accessing a causal model as explaining itself.
To complement our analysis, we also provide relevant software solutions used to
automate causal tasks. We believe our work provides a unified view of the two
fields of causality and XAI by highlighting potential domain bridges and
uncovering possible limitations.
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