black difficult reasoning behind defensibility
Research gap analysis derived from 2 computer_science papers in our local library.
The gap
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.; 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.
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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