Inherent Inconsistencies of Feature Importance
- URL: http://arxiv.org/abs/2206.08204v2
- Date: Tue, 5 Dec 2023 22:29:53 GMT
- Title: Inherent Inconsistencies of Feature Importance
- Authors: Nimrod Harel, Uri Obolski, Ran Gilad-Bachrach
- Abstract summary: Feature importance is a method that assigns scores to the contribution of individual features on prediction outcomes.
This paper presents an axiomatic framework designed to establish coherent relationships among the different contexts of feature importance scores.
- Score: 6.02357145653815
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The rapid advancement and widespread adoption of machine learning-driven
technologies have underscored the practical and ethical need for creating
interpretable artificial intelligence systems. Feature importance, a method
that assigns scores to the contribution of individual features on prediction
outcomes, seeks to bridge this gap as a tool for enhancing human comprehension
of these systems. Feature importance serves as an explanation of predictions in
diverse contexts, whether by providing a global interpretation of a phenomenon
across the entire dataset or by offering a localized explanation for the
outcome of a specific data point. Furthermore, feature importance is being used
both for explaining models and for identifying plausible causal relations in
the data, independently from the model. However, it is worth noting that these
various contexts have traditionally been explored in isolation, with limited
theoretical foundations.
This paper presents an axiomatic framework designed to establish coherent
relationships among the different contexts of feature importance scores.
Notably, our work unveils a surprising conclusion: when we combine the proposed
properties with those previously outlined in the literature, we demonstrate the
existence of an inconsistency. This inconsistency highlights that certain
essential properties of feature importance scores cannot coexist harmoniously
within a single framework.
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