Metric Tools for Sensitivity Analysis with Applications to Neural
Networks
- URL: http://arxiv.org/abs/2305.02368v1
- Date: Wed, 3 May 2023 18:10:21 GMT
- Title: Metric Tools for Sensitivity Analysis with Applications to Neural
Networks
- Authors: Jaime Pizarroso and David Alfaya and Jos\'e Portela and Antonio
Mu\~noz
- Abstract summary: Explainable Artificial Intelligence (XAI) aims to provide interpretations for predictions made by Machine Learning models.
In this paper, a theoretical framework is proposed to study sensitivities of ML models using metric techniques.
A complete family of new quantitative metrics called $alpha$-curves is extracted.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As Machine Learning models are considered for autonomous decisions with
significant social impact, the need for understanding how these models work
rises rapidly. Explainable Artificial Intelligence (XAI) aims to provide
interpretations for predictions made by Machine Learning models, in order to
make the model trustworthy and more transparent for the user. For example,
selecting relevant input variables for the problem directly impacts the model's
ability to learn and make accurate predictions, so obtaining information about
input importance play a crucial role when training the model. One of the main
XAI techniques to obtain input variable importance is the sensitivity analysis
based on partial derivatives. However, existing literature of this method
provide no justification of the aggregation metrics used to retrieved
information from the partial derivatives.
In this paper, a theoretical framework is proposed to study sensitivities of
ML models using metric techniques. From this metric interpretation, a complete
family of new quantitative metrics called $\alpha$-curves is extracted. These
$\alpha$-curves provide information with greater depth on the importance of the
input variables for a machine learning model than existing XAI methods in the
literature. We demonstrate the effectiveness of the $\alpha$-curves using
synthetic and real datasets, comparing the results against other XAI methods
for variable importance and validating the analysis results with the ground
truth or literature information.
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