Explainable AI for time series classification: a review, taxonomy and research directions
Time series data is increasingly used in a wide range of fields, and it is often relied on in
crucial applications and high-stakes decision-making. For instance, sensors generate time …
crucial applications and high-stakes decision-making. For instance, sensors generate time …
Xair: A systematic metareview of explainable ai (xai) aligned to the software development process
Currently, explainability represents a major barrier that Artificial Intelligence (AI) is facing in
regard to its practical implementation in various application domains. To combat the lack of …
regard to its practical implementation in various application domains. To combat the lack of …
[HTML][HTML] Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …
applications, but the outcomes of many AI models are challenging to comprehend and trust …
From attribution maps to human-understandable explanations through concept relevance propagation
The field of explainable artificial intelligence (XAI) aims to bring transparency to today's
powerful but opaque deep learning models. While local XAI methods explain individual …
powerful but opaque deep learning models. While local XAI methods explain individual …
Pan-cancer integrative histology-genomic analysis via multimodal deep learning
The rapidly emerging field of computational pathology has demonstrated promise in
developing objective prognostic models from histology images. However, most prognostic …
developing objective prognostic models from histology images. However, most prognostic …
Look before you leap: An exploratory study of uncertainty measurement for large language models
The recent performance leap of Large Language Models (LLMs) opens up new
opportunities across numerous industrial applications and domains. However, erroneous …
opportunities across numerous industrial applications and domains. However, erroneous …
A machine learning approach for predicting hidden links in supply chain with graph neural networks
EE Kosasih, A Brintrup - International Journal of Production …, 2022 - Taylor & Francis
Supply chain business interruption has been identified as a key risk factor in recent years,
with high-impact disruptions due to disease outbreaks, logistic issues such as the recent …
with high-impact disruptions due to disease outbreaks, logistic issues such as the recent …
[HTML][HTML] Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing
I Kakogeorgiou, K Karantzalos - … Journal of Applied Earth Observation and …, 2021 - Elsevier
Although deep neural networks hold the state-of-the-art in several remote sensing tasks,
their black-box operation hinders the understanding of their decisions, concealing any bias …
their black-box operation hinders the understanding of their decisions, concealing any bias …
Deep learning supported discovery of biomarkers for clinical prognosis of liver cancer
Tissue biomarkers are crucial for cancer diagnosis, prognosis assessment and treatment
planning. However, there are few known biomarkers that are robust enough to show true …
planning. However, there are few known biomarkers that are robust enough to show true …
[HTML][HTML] Interpretability of deep learning models for crop yield forecasting
Abstract Machine learning models for crop yield forecasting often rely on expert-designed
features or predictors. The effectiveness and interpretability of these handcrafted features …
features or predictors. The effectiveness and interpretability of these handcrafted features …