explainable ai

EXACT: An Explainable Adaptive SemantiC-Trace framework for AI-generated image forensics

Towards explainable AI-driven individualized treatment optimization for breast cancer time-to-event outcomes

Robotic technologies for sewer inspection: From automated assessment to explainable AI-enabled solutions

Artificial Intelligence in Spinal Cord Stimulation and Neuromodulation: A Narrative Review of Clinical Applications, Emerging Evidence, and Future Directions

This narrative review article uniquely integrates current and emerging applications of AI across full SCS pathway while also further critically highlighting evidence gaps, future directions for precision neuromodulation.

Explainable AI for Smart Grid Intelligence: A Comprehensive Review of Techniques, Applications and Future Directions

Precision agriculture meets explainable AI: A highly competitive ET0 prediction framework based on hybrid deep learning

An explainable AI framework for identifying photophysical signatures of nanomaterial maturation via transient absorption spectroscopy

On the Interplay of Explainability and Fairness in AI: A Survey

Algorithmic fairness and explainability are foundational pillars of responsible AI. Although often studied independently, their interplay is increasingly recognized as crucial for diagnosing and mitigating bias in machine learning systems. We first introduce two systematic taxonomies: one for algorithmic fairness and one for explainable AI, to organize the landscape of existing work across diverse tasks (classification, ranking, and recommendation) and data modalities (tabular, graph). Next, we categorize the use of explanations in fairness efforts into three main functions: (a) detecting and understanding the causes of unfairness, (b) defining enhanced fairness metrics, and (c) designing mitigation strategies. In addition, we examine how explanation methods themselves can be biased, underscoring the need to evaluate fairness for explanations. Finally, we identify open research challenges and outline promising directions for future research at the intersection of fairness and explainability.

Explainable AI in solar PV forecasting: A systematic review of models, explainers, and physical drivers

Emotion detection using AI with explainable AI based perception: A comparative analysis between machine learning and deep learning

Engineering an integrated biosensing interface combining DNA-assisted clustering and explainable AI for biomarker detection.

An integrated biosensing framework that treats readout reliability as an explicit engineering objective rather than a post hoc correction problem, and establishes a generalizable strategy for constructing trustworthy POCT systems in which chemical signal generation and digital interpretation are co-designed.

Transparency and Explainability in Human Factors — A Systematic Review of Usability Assessment Practices for AI Medical Devices

Results identify a "symmetry of modality": qualitative interviews correlate with written text explanations, while Think-Aloud protocols better assess cognitively demanding tools like SHAP values.