Anomaly detection in average fuel consumption with XAI techniques for dynamic generation of explanations.
Year: 2020
Type: preprint
Abstract: In this paper we show a complete process for unsupervised anomaly detectionfor the average fuel consumption of fleet vehicles that is able to explain what
variables are affecting the consumption in terms of feature relevance. For
doing that, we combine the anomaly detection with a surrogate model that is
able to provide that feature relevance. For this part, we evaluate both
whitebox models from the literature, as well as novel variations over them, and
blackbox models combined with local posthoc feature relevance techniques. The
evaluation is done using real IoT data belonging to Telef\'onica, and is
measured both in terms of model performance, as well as using Explainable AI
metrics that compare the explanations generated in terms representativeness,
fidelity, stability and contrastiveness. The explanations generate
counterfactual recommendations that show what could have been done to reduce
the average fuel consumption of a vehicle and turn it into an inlier. The
procedure is combined with domain knowledge expressed in business rules, and is
able to adequate the type of explanations depending on the target user profile.
Source: arXiv (Cornell University)
Author: Alberto Barbado
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3095447762