Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing
  Human Trust in Image Recognition Models   We propose CX-ToM, short for counterfactual explanations with theory-of mind,
a new explainable AI (XAI) framework for explaining decisions made by a deep
convolutional neural network (CNN). In contrast to the current methods in XAI
that generate explanations as a single shot response, we pose explanation as an
iterative communication process, i.e. dialog, between the machine and human
user. More concretely, our CX-ToM framework generates sequence of explanations
in a dialog by mediating the differences between the minds of machine and human
user. To do this, we use Theory of Mind (ToM) which helps us in explicitly
modeling human's intention, machine's mind as inferred by the human as well as
human's mind as inferred by the machine. Moreover, most state-of-the-art XAI
frameworks provide attention (or heat map) based explanations. In our work, we
show that these attention based explanations are not sufficient for increasing
human trust in the underlying CNN model. In CX-ToM, we instead use
counterfactual explanations called fault-lines which we define as follows:
given an input image I for which a CNN classification model M predicts class
c_pred, a fault-line identifies the minimal semantic-level features (e.g.,
stripes on zebra, pointed ears of dog), referred to as explainable concepts,
that need to be added to or deleted from I in order to alter the classification
category of I by M to another specified class c_alt. We argue that, due to the
iterative, conceptual and counterfactual nature of CX-ToM explanations, our
framework is practical and more natural for both expert and non-expert users to
understand the internal workings of complex deep learning models. Extensive
quantitative and qualitative experiments verify our hypotheses, demonstrating
that our CX-ToM significantly outperforms the state-of-the-art explainable AI
models.

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The categories of this paper are: math-ph
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The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: atom-ph
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The categories of this paper are: adap-org
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The categories of this paper are: math
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The categories of this paper are: stat
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The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: acc-phys
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The categories of this paper are: cond-mat
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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-th