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How Unstable Is LIME? Evaluating Sensitivity and Trust in Explainable AI
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| 2.27 MB | Adobe PDF |
Orientador(es)
Resumo(s)
As machine learning systems permeate high-stakes fields such as healthcare, finance, and
public policy, the demand for transparent and interpretable algorithms has intensified, giving
rise to Explainable Artificial Intelligence (XAI) techniques that shed light on opaque “black‐
box” models. Among these, LIME (Local Interpretable Model‐Agnostic Explanations) stands
out for generating simplified surrogate models around individual predictions, yet its
dependence on randomized perturbations compromises its reliability: slight variations in
random seeds or sampling strategies can produce divergent explanations for the same input.
To quantify this instability, we designed a unified experimental framework evaluating LIME’s
numerical consistency on four tabular datasets, Titanic, Iris, Wine Quality, and California
Housing, using Random Forests for classification and regression tasks. We applied LIME
repeatedly to identical instances under controlled conditions, assessing stability through both
visual diagnostics (feature‐weight trajectories, boxplots, and frequency histograms) and
quantitative measures (feature‐weight standard deviations, Top‐1 feature recurrence rate,
and local approximation errors). Our results indicate that while LIME delivers stable, coherent
feature attributions in simple classification scenarios, its explanations fluctuate markedly in
complex regression contexts, with high variability in feature weights and approximation
fidelity despite recurring top‐ranked predictors. By systematically documenting LIME’s
context‐dependent variability and proposing a reproducible evaluation protocol, this thesis
highlights a critical limitation of a popular XAI tool and underscores the necessity of rigorous
stability checks before deploying local explanation methods in sensitive or regulated
environments.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Explainable Artificial Intelligence (XAI) LIME Model interpretability Stability of explanations Tabular data Local surrogate models Feature attribution Machine learning evaluation