Black-box AI Models
Research gap analysis derived from 2 computer_science papers in our local library.
The gap
There is a need for explainable artificial intelligence (XAI) techniques in various applications such as medical imaging, cybersecurity, and education to improve transparency and trustworthiness of black-box models.
Consensus across the literature
The papers collectively establish that black-box AI models limit interpretability and trust but leave open the specific methods and frameworks needed to address this issue.
Research trend
Emerging — attention growing, methods still coalescing.
Supporting evidence — 2 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
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