Inside the black box: A comprehensive survey of explainable AI benchmarks, protocols, technical analysis, and open gaps

AI and Bullshit

It is argued that both AI and bullshitters are untrustworthy informants, and for similar reasons, it is natural to describe AI’s informational outputs as bullshit, as it signals their distinctive kind of epistemic deficiencies, which they share with bullshit.

Reinforcement Loads Prediction of Geosynthetic-Reinforced Soil Structures Using Explainable and Nonexplainable Machine Learning Approaches

This study presents three advanced machine learning models: the evolutionary Gaussian process inference model, the artificial satellite search algorithm–moment balance machine (ASSA-MBM), and the Operation Rain Forest (ORF), which are designed to predict the maximum reinforcement load in geosynthetic-reinforced soil structures. These models were developed to enhance both predictive accuracy and model interpretability by incorporating state-of-the-art optimization algorithms and explainable machine learning frameworks. A comprehensive evaluation was conducted using 10-fold cross-validation, and the proposed models were benchmarked against previously developed AI models from literature, as well as traditional and semiempirical approaches such as Rankine, Coulomb, and K -stiffness. Among the proposed models, ASSA-MBM consistently achieved the best performance, recording the lowest testing root mean squared error (0.617), the highest correlation coefficient ( R = 0.918 ), and the highest reference index ( RI = 0.951 ). Additionally, the ORF model offers transparency by generating mathematical regression equations, which are crucial in geotechnical engineering.

Artificial intelligence as a strategic dynamic capability for enhancing triple bottom line performance in MSMES

The results indicate that sustainable performance is achieved not merely through AI adoption but through the organization's ability to sense opportunities, seize strategic initiatives and continuously reconfigure resources, thereby extending RBV and DCT within the sustainability and digital transformation domains.

Deep Learning in Medical Imaging: Architectures, Clinical Applications, and Emerging Directions

Major deep learning architectures, including CNNs, residual networks, UNet, attention-based models, Vision Transformers, and hybrid approaches, along with their clinical applications are summarized and emerging directions such as self-supervised learning, Explainable AI, federated learning, and lightweight models are highlighted as promising approaches for more reliable and accessible medical image analysis.

Deep-synthesized image detection via multi-domain feature fusion and explainable AI guided color model selection

An explainable generative AI framework for detecting low-rate API-based DDoS attacks in cloud environments