Longitudinal Distance: Towards Accountable Instance Attribution

• URL: http://arxiv.org/abs/2108.10437v1
• Date: Mon, 23 Aug 2021 22:50:23 GMT
• Title: Longitudinal Distance: Towards Accountable Instance Attribution
• Authors: Rosina O. Weber, Prateek Goel, Shideh Amiri, and Gideon Simpson
• Abstract summary: This paper introduces a pseudo-metric we call Longitudinal distance and its use to attribute instances to a neural network agent's decision. Inspired in case-based reasoning principles, this paper introduces a pseudo-metric we call Longitudinal distance and its use to attribute instances to a neural network agent's decision.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Previous research in interpretable machine learning (IML) and explainable artificial intelligence (XAI) can be broadly categorized as either focusing on seeking interpretability in the agent's model (i.e., IML) or focusing on the context of the user in addition to the model (i.e., XAI). The former can be categorized as feature or instance attribution. Example- or sample-based methods such as those using or inspired by case-based reasoning (CBR) rely on various approaches to select instances that are not necessarily attributing instances responsible for an agent's decision. Furthermore, existing approaches have focused on interpretability and explainability but fall short when it comes to accountability. Inspired in case-based reasoning principles, this paper introduces a pseudo-metric we call Longitudinal distance and its use to attribute instances to a neural network agent's decision that can be potentially used to build accountable CBR agents.

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