Results 71 to 80 of about 24,682 (247)

Shapley Additive Explanation for Local Class Differentiation: Local Explainability for Class Differentiation in Classification Models

Advanced Intelligent Systems, EarlyView.
An instance‐level, model‐agnostic explanation of class differentiation is introduced through SHAP‐LCD, linking probability shifts to feature‐wise Shapley contributions. The method operates on tabular and image data and is released in a fully reproducible implementation, offering a transparent way to examine, at each instance, why predictive models ...
Roxana M. Romero Luna, Hugo A. Ordoñez Erazo, Carlos A. Cobos Lozada   +2 more
wiley   +1 more source

Mitigating Cognitive Biases in Predicting Student Dropout: Global and Local Explainability with Explainable Boosting Machine [PDF]

Journal of Universal Computer Science
This study explores the application of Explainable Artificial Intelligence (XAI) techniques to mitigate cognitive biases in predicting student dropout. Focusing on the Explainable Boosting Machine (EBM), we compare its performance and explainability with
Rodrigo Costa Camargos, Ismar Frango Silveira   +1 more
doaj   +3 more sources

Addressing Small Data Challenges in Biopharmaceutical Development and Manufacturing: A Mini Review of Multi‐Fidelity Techniques

Biotechnology and Bioengineering, EarlyView.
ABSTRACT The growing demand for biopharmaceutical products reflects their effectiveness in medical treatments. However, developing new biopharmaceuticals remains a major bottleneck, often taking up to a decade before market approval. Machine learning (ML) models have the potential to accelerate this process, but their success depends on access to large
Mohammad Golzarijalal, Uwe Aickelin, Ellen Otte   +2 more
wiley   +1 more source

Understanding the dilemma of explainable artificial intelligence: a proposal for a ritual dialog framework

Humanities & Social Sciences Communications
This paper addresses how people understand Explainable Artificial Intelligence (XAI) in three ways: contrastive, functional, and transparent. We discuss the unique aspects and challenges of each and emphasize improving current XAI understanding ...
Aorigele Bao, Yi Zeng
doaj   +1 more source

Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review

BMJ Health & Care Informatics
Background Breast cancer is the most common disease in women. Recently, explainable artificial intelligence (XAI) approaches have been dedicated to investigate breast cancer. An overwhelming study has been done on XAI for breast cancer.
Worku Jimma, Daraje kaba Gurmessa
doaj   +1 more source

Explainable Artificial Intelligence (XAI): A reason to believe?

Law in Context. A Socio-legal Journal, 2022
Artificial intelligence is an alluring technology which companies and governments hope to benefit from. In many circumstances a condition of its use is that humans can understand an explanation of why the action of an AI system took place. This has encouraged the development of a field of “explainable artificial intelligence”, or XAI.
openaire   +1 more source

Artificial Intelligence–Driven and Digital Practices for Circular Business and Finance: Insights for Advancing Hubs for Circularity

Business Strategy and the Environment, EarlyView.
ABSTRACT The emerging concept of Hubs for Circularity (H4Cs) presents an opportunity to create collaborative, self‐sustaining regional industrial ecosystems that drive circular economy transitions at scale. However, the operationalisation of H4Cs faces financial, organisational and data‐driven challenges.
Aditya Tripathi, Marcos R. Machado, Laura Spierdijk, Devrim Murat Yazan   +3 more
wiley   +1 more source

Effectiveness of Explainable Artificial Intelligence (XAI) Techniques for Improving Human Trust in Machine Learning Models: A Systematic Literature Review

IEEE Access
Most decision-making processes worldwide are increasingly relying on artificial intelligence (AI) algorithms to enhance human welfare. Explainable Artificial Intelligence (XAI) techniques are pivotal in addressing the bottlenecks of utilizing machine ...
In-On Wiratsin, Chaiyong Ragkhitwetsagul
doaj   +1 more source

Regulating Explainable Artificial Intelligence (XAI) May Harm Consumers

Marketing Science
This work analyzes the impact of regulating explainable artificial intelligence.
Behnam Mohammadi, Nikhil Malik, Tim Derdenger, Kannan Srinivasan   +3 more
openaire   +2 more sources

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source