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Navid Nobani

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

IJCAI Conference 2022 Conference Paper

The Good, the Bad, and the Explainer: A Tool for Contrastive Explanations of Text Classifiers

Lorenzo Malandri
Fabio Mercorio
Mario Mezzanzanica
Navid Nobani
Andrea Seveso

In the last few years, we have been witnessing the increasing deployment of machine learning-based systems, which act as black boxes whose behaviour is hidden to end-users. As a side-effect, this contributes to increasing the need for explainable methods and tools to support the coordination between humans and ML models towards collaborative decision-making. In this paper, we demonstrate ContrXT, a novel tool that computes the differences in the classification logic of two distinct trained models, reasoning on their symbolic representation through Binary Decision Diagrams. ContrXT is available as a pip package and API.

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AAAI Conference 2021 Short Paper

A Method for Taxonomy-Aware Embeddings Evaluation (Student Abstract)

Navid Nobani
Lorenzo Malandri
Fabio Mercorio
Mario Mezzanzanica

While word embeddings have been showing their effectiveness in capturing semantic and lexical similarities in a large number of domains, in case the corpus used to generate embeddings is associated with a taxonomy (i. e. , classification tasks over standard de-jure taxonomies) the common intrinsic and extrinsic evaluation tasks cannot guarantee that the generated embeddings are consistent with the taxonomy. This, as a consequence, sharply limits the use of distributional semantics in those domains. To address this issue, we design and implement MEET, which proposes a new measure -HSS- that allows evaluating embeddings from a text corpus preserving the semantic similarity relations of the taxonomy.

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IJCAI Conference 2021 Conference Paper

Towards an Explainer-agnostic Conversational XAI

Navid Nobani
Fabio Mercorio
Mario Mezzanzanica

Explainable Artificial Intelligence (XAI) is gaining interests in both academia and industry, mainly thanks to the proliferation of darker more complex black-box solutions which are replacing their more transparent ancestors. Believing that the overall performance of an XAI system can be augmented by considering the end-user as a human being, we are studying the ways we can improve the explanations by making them more informative and easier to use from one hand, and interactive and customisable from the other hand.

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