Explainable AI techniques such as SHAP and LIME should be implemented to improve model interpretability and transparency.
Research gap analysis derived from 3 computer_science papers in our local library.
The gap
Explainable AI techniques such as SHAP and LIME should be implemented to improve model interpretability and transparency.
Consensus across the literature
Clustered from 4 gap mentions across 3 papers via embedding cosine ≥ 0.62.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 4 representative gaps
- Artificial Intelligence-Driven Environmental Toxicology: Predictive Toxicity Modelling, Forensic Pollution Analysis, and AI-Enabled Public Health Surveillance (2026) · doi
Black-box AI models are difficult to interpret because the reasoning or process behind conclusions is unknown, and absence of explainable AI (XAI) limits defensibility.
Keywords: black models difficult interpret reasoning process behind conclusions unknown absence explainable limits defensibility - The Role of Machine Learning in Cyber Security (2026) · doi
Many ML algorithms, particularly deep learning models, are considered 'black boxes' due to their complex architectures, making it difficult to understand the reasoning behind ML model decisions and hindering the ability to trust and explain their predictions.
Keywords: algorithms particularly deep learning models considered black boxes complex architectures making difficult understand reasoning behind - Enhancing Breast Cancer Diagnosis through Machine Learning: A Robust Approach for Early Detection (2026) · doi
The machine learning models lack explainability mechanisms to help healthcare professionals understand feature contributions to breast cancer predictions. Implementing Explainable AI (XAI) techniques to increase model transparency and enable clinicians to interpret Random Forest and XGBoost predictions is identified as necessary but not yet integrated into the diagnostic system.
Keywords: Explainable AI XAI model interpretability Random Forest feature importance healthcare transparency - Artificial Intelligence-Driven Environmental Toxicology: Predictive Toxicity Modelling, Forensic Pollution Analysis, and AI-Enabled Public Health Surveillance (2026) · doi
Explainable AI techniques such as SHAP and LIME should be implemented to improve model interpretability and transparency.
Keywords: explainable techniques shap lime implemented improve model interpretability transparency
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