Explainable artificial intelligence
206 papers with code • 0 benchmarks • 8 datasets
XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.
Benchmarks
These leaderboards are used to track progress in Explainable artificial intelligence
Libraries
Use these libraries to find Explainable artificial intelligence models and implementations
BDD-X
e-SNLI-VE
OpenXAI
e-ViL
AnthroProtect
ExBAN
EUCA dataset
Latest papers
Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive Bias
To overcome this limitation, we introduce Multi-Excitation Projective Simulation (mePS), a generalization that considers a chain-of-thought to be a random walk of several particles on a hypergraph.
Detecting mental disorder on social media: a ChatGPT-augmented explainable approach
In the digital era, the prevalence of depressive symptoms expressed on social media has raised serious concerns, necessitating advanced methodologies for timely detection.
NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble Techniques
This paper presents a comprehensive comparative analysis of explainable artificial intelligence (XAI) ensembling methods.
Deep Learning for Gamma-Ray Bursts: A data driven event framework for X/Gamma-Ray analysis in space telescopes
This thesis comprises the first three chapters dedicated to providing an overview of Gamma Ray-Bursts (GRBs), their properties, the instrumentation used to detect them, and Artificial Intelligence (AI) applications in the context of GRBs, including a literature review and future prospects.
Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles
While shallow decision trees may be interpretable, larger ensemble models like gradient-boosted trees, which often set the state of the art in machine learning problems involving tabular data, still remain black box models.
Word-Level ASR Quality Estimation for Efficient Corpus Sampling and Post-Editing through Analyzing Attentions of a Reference-Free Metric
aixplain/NoRefER
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The findings suggest that NoRefER is not merely a tool for error detection but also a comprehensive framework for enhancing ASR systems' transparency, efficiency, and effectiveness.
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment
tommy-bie/mica
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Black-box deep learning approaches have showcased significant potential in the realm of medical image analysis.
Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test
annahedstroem/sanity-checks-revisited
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The Model Parameter Randomisation Test (MPRT) is widely acknowledged in the eXplainable Artificial Intelligence (XAI) community for its well-motivated evaluative principle: that the explanation function should be sensitive to changes in the parameters of the model function.
Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space
We propose a design space for XAI4BCI, considering the evolving need to visualize and investigate predictive model outcomes customised for various stakeholders in the BCI development and deployment lifecycle.
An Interpretable Deep Learning Approach for Skin Cancer Categorization
Our models decision-making process can be clarified because of the implementation of explainable artificial intelligence (XAI).
