7 research outputs found
Calibrated Explanations: with Uncertainty Information and Counterfactuals
While local explanations for AI models can offer insights into individual
predictions, such as feature importance, they are plagued by issues like
instability. The unreliability of feature weights, often skewed due to poorly
calibrated ML models, deepens these challenges. Moreover, the critical aspect
of feature importance uncertainty remains mostly unaddressed in Explainable AI
(XAI). The novel feature importance explanation method presented in this paper,
called Calibrated Explanations (CE), is designed to tackle these issues
head-on. Built on the foundation of Venn-Abers, CE not only calibrates the
underlying model but also delivers reliable feature importance explanations
with an exact definition of the feature weights. CE goes beyond conventional
solutions by addressing output uncertainty. It accomplishes this by providing
uncertainty quantification for both feature weights and the model's probability
estimates. Additionally, CE is model-agnostic, featuring easily comprehensible
conditional rules and the ability to generate counterfactual explanations with
embedded uncertainty quantification. Results from an evaluation with 25
benchmark datasets underscore the efficacy of CE, making it stand as a fast,
reliable, stable, and robust solution.Comment: 19 pages, 6 figures, 3 tables, submitted to journa
Conformal Prediction: a Unified Review of Theory and New Challenges
In this work we provide a review of basic ideas and novel developments about
Conformal Prediction -- an innovative distribution-free, non-parametric
forecasting method, based on minimal assumptions -- that is able to yield in a
very straightforward way predictions sets that are valid in a statistical sense
also in in the finite sample case. The in-depth discussion provided in the
paper covers the theoretical underpinnings of Conformal Prediction, and then
proceeds to list the more advanced developments and adaptations of the original
idea.Comment: arXiv admin note: text overlap with arXiv:0706.3188,
arXiv:1604.04173, arXiv:1709.06233, arXiv:1203.5422 by other author
Well-calibrated Confidence Measures for Multi-label Text Classification with a Large Number of Labels
We extend our previous work on Inductive Conformal Prediction (ICP) for
multi-label text classification and present a novel approach for addressing the
computational inefficiency of the Label Powerset (LP) ICP, arrising when
dealing with a high number of unique labels. We present experimental results
using the original and the proposed efficient LP-ICP on two English and one
Czech language data-sets. Specifically, we apply the LP-ICP on three deep
Artificial Neural Network (ANN) classifiers of two types: one based on
contextualised (bert) and two on non-contextualised (word2vec) word-embeddings.
In the LP-ICP setting we assign nonconformity scores to label-sets from which
the corresponding p-values and prediction-sets are determined. Our approach
deals with the increased computational burden of LP by eliminating from
consideration a significant number of label-sets that will surely have p-values
below the specified significance level. This reduces dramatically the
computational complexity of the approach while fully respecting the standard CP
guarantees. Our experimental results show that the contextualised-based
classifier surpasses the non-contextualised-based ones and obtains
state-of-the-art performance for all data-sets examined. The good performance
of the underlying classifiers is carried on to their ICP counterparts without
any significant accuracy loss, but with the added benefits of ICP, i.e. the
confidence information encapsulated in the prediction sets. We experimentally
demonstrate that the resulting prediction sets can be tight enough to be
practically useful even though the set of all possible label-sets contains more
than combinations. Additionally, the empirical error rates of the
obtained prediction-sets confirm that our outputs are well-calibrated