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ConvXAI: a System for Multimodal Interaction with Any Black-box Explainer

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Abstract

Several studies have addressed the importance of context and users’ knowledge and experience in quantifying the usability and effectiveness of the explanations generated by explainable artificial intelligence (XAI) systems. However, to the best of our knowledge, no component-agnostic system that accounts for this need has yet been built. This paper describes an approach called ConvXAI, which can create a dialogical multimodal interface for any black-box explainer by considering the knowledge and experience of the user. First, we formally extend the state-of-the-art conversational explanation framework by introducing clarification dialogue as an additional dialogue type. We then implement our approach as an off-the-shelf Python tool. To evaluate our framework, we performed a user study including 45 participants divided into three groups based on their level of technology use and job function. Experimental results show that (i) different groups perceive explanations differently; (ii) all groups prefer textual explanations over graphical ones; and (iii) ConvXAI provides clarifications that enhance the usefulness of the original explanations.

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Data Availability

The datasets analysed during the current study are available at https://bit.ly/3u4hFLM.

Notes

  1. https://bit.ly/3haAXM3

  2. https://rasa.com/

  3. https://www.luis.ai/

  4. All datasets can be accessed from https://github.com/sebischair/NLU-Evaluation-Corpora

  5. https://cloud.google.com/dialogflow

  6. https://bit.ly/3haAXM3

  7. https://forms.gle/JArrvVVYJR1jdpUs8

  8. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai

Abbreviations

XAI:

Explainable Artificial Intelligence

NL:

Natural Language

NLP:

Natural Language Processing

NLG:

Natural Language Generation

NLU:

Natural Language Understanding

ADF:

Agent Dialogue Framework

SLU:

Spoken Language Understanding

NN:

Neural Network

RecNN:

Recursive Neural Network

CNN:

Convolutional Neural Networks

CRF:

Conditional Random Fields

TriCRF:

Triangular CRF

GRU:

Gated Recurrent Unit

RNN:

Recurrent Neural Network

BLSTM:

Bidirectional Long Short-term Memory

BERT:

Bidirectional Encoder Representations from Transformers

DST:

Dialogue State Tracking

Seq2Seq:

Sequence-to-sequence

ASR:

Automatic Speech Recognition

DP:

Dialogue Policy

EDM:

Explanation Dialogue Model

UML:

Unified Modeling Language

ML:

Machine Learning

CI:

Conversation Initialiser

SVM:

Support Vector Machines

EG:

Explanation Generator

JSON:

JavaScript Object Notation

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Authors and Affiliations

  1. Dept. of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy

    Lorenzo Malandri, Fabio Mercorio & Mario Mezzanzanica

  2. CRISP Research Centre, University of Milan-Bicocca, Milan, Italy

    Lorenzo Malandri, Fabio Mercorio & Mario Mezzanzanica

  3. http://www.crispresearch.eu

    Lorenzo Malandri, Fabio Mercorio & Mario Mezzanzanica

  4. Dept. of Informatics, Systems & Communication, University of Milan-Bicocca, Milan, Italy

    Navid Nobani

  5. Digital Attitude S.r.l, Milan, Italy

    Navid Nobani

  6. https://www.habitinspiringplatform.com/

    Navid Nobani

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  1. Lorenzo Malandri
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Malandri, L., Mercorio, F., Mezzanzanica, M. et al. ConvXAI: a System for Multimodal Interaction with Any Black-box Explainer. Cogn Comput 15, 613–644 (2023). https://doi.org/10.1007/s12559-022-10067-7

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