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Found 18 papers, 9 papers with code
Bias Discovery within Human Raters: A Case Study of the Jigsaw Dataset
Understanding and quantifying the bias introduced by human annotation of data is a crucial problem for trustworthy supervised learning.
A Bag of Receptive Fields for Time Series Extrinsic Predictions
High-dimensional time series data poses challenges due to its dynamic nature, varying lengths, and presence of missing values.
Social Bias Probing: Fairness Benchmarking for Language Models
While the impact of these biases has been recognized, prior methods for bias evaluation have been limited to binary association tests on small datasets, offering a constrained view of the nature of societal biases within language models.
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Ensemble of Counterfactual Explainers
In eXplainable Artificial Intelligence (XAI), several counterfactual explainers have been proposed, each focusing on some desirable properties of counterfactual instances: minimality, actionability, stability, diversity, plausibility, discriminative power.
A Protocol for Continual Explanation of SHAP
Continual Learning trains models on a stream of data, with the aim of learning new information without forgetting previous knowledge.
Boosting Synthetic Data Generation with Effective Nonlinear Causal Discovery
However, typically, the variables of a dataset depend on one another, and these dependencies are not considered in data generation leading to the creation of implausible records.
Exemplars and Counterexemplars Explanations for Image Classifiers, Targeting Skin Lesion Labeling
We propose a use case study, for skin lesion diagnosis, illustrating how it is possible to provide the practitioner with explanations on the decisions of a state of the art deep neural network classifier trained to characterize skin lesions from examples.
Quantum Clustering with k-Means: a Hybrid Approach
In particular, we exploit quantum phenomena to speed up the computation of distances.
Causality-Aware Local Interpretable Model-Agnostic Explanations
A main drawback of eXplainable Artificial Intelligence (XAI) approaches is the feature independence assumption, hindering the study of potential variable dependencies.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Explainable Deep Image Classifiers for Skin Lesion Diagnosis
A key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making systems.
Benchmarking and Survey of Explanation Methods for Black Box Models
The widespread adoption of black-box models in Artificial Intelligence has enhanced the need for explanation methods to reveal how these obscure models reach specific decisions.
GLocalX -- From Local to Global Explanations of Black Box AI Models
Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other.
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space
We present an approach to explain the decisions of black box models for image classification.
On The Stability of Interpretable Models
Interpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent.
Open the Black Box Data-Driven Explanation of Black Box Decision Systems
We introduce the local-to-global framework for black box explanation, a novel approach with promising early results, which paves the road for a wide spectrum of future developments along three dimensions: (i) the language for expressing explanations in terms of highly expressive logic-based rules, with a statistical and causal interpretation; (ii) the inference of local explanations aimed at revealing the logic of the decision adopted for a specific instance by querying and auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of the many local explanations into simple global ones, with algorithms that optimize the quality and comprehensibility of explanations.
Local Rule-Based Explanations of Black Box Decision Systems
Then it derives from the logic of the local interpretable predictor a meaningful explanation consisting of: a decision rule, which explains the reasons of the decision; and a set of counterfactual rules, suggesting the changes in the instance's features that lead to a different outcome.
A Survey Of Methods For Explaining Black Box Models
The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, delineating explicitly or implicitly its own definition of interpretability and explanation.
