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Argflow: A Toolkit for Deep Argumentative Explanations for Neural Networks

Authors:

Piyawat Lertvittayakumjorn,

Antonio Rago,

Francesca ToniAuthors Info & Claims

AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems

Pages 1761 - 1763

Published: 03 May 2021 Publication History

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Abstract

In recent years, machine learning (ML) models have been successfully applied in a variety of real-world applications. However, they are often complex and incomprehensible to human users. This can decrease trust in their outputs and render their usage in critical settings ethically problematic. As a result, several methods for explaining such ML models have been proposed recently, in particular for black-box models such as deep neural networks (NNs). Nevertheless, these methods predominantly explain model outputs in terms of inputs, disregarding the inner workings of the ML model computing those outputs. We present Argflow, a toolkit enabling the generation of a variety of 'deep' argumentative explanations (DAXs) for outputs of NNs on classification tasks.

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Cited By

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Jiang JLeofante FRago AToni FDastani MSichman JAlechina NDignum V(2024)Recourse under Model Multiplicity via Argumentative EnsemblingProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662950(954-963)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662950

Index Terms

Argflow: A Toolkit for Deep Argumentative Explanations for Neural Networks
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interactive systems and tools
  2. Visualization
    1. Visualization systems and tools

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Published In

AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems

May 2021

1899 pages

ISBN:9781450383073

General Chairs:
Frank Dignum
Umeå University, Sweden
,
Alessio Lomuscio
Imperial College London, UK
,
Program Chairs:
Ulle Endriss
University of Amsterdam, Netherlands
,
Ann Nowé
Vrije Universiteit Brussel, Belgium

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International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

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Published: 03 May 2021

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AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems

May 3 - 7, 2021

Virtual Event, United Kingdom

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Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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Jiang JLeofante FRago AToni FDastani MSichman JAlechina NDignum V(2024)Recourse under Model Multiplicity via Argumentative EnsemblingProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662950(954-963)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662950

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Neural QBAFs: Explaining Neural Networks Under LRP-Based Argumentation Frameworks

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