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
Explore this gap further
Search “Black-box AI Models” across open scholarly engines for the latest related literature.
Working on this gap? Publish with us.
Science AI Journal reviews manuscripts in under 15 minutes with 8 specialised AI reviewers calibrated on 23,000+ real peer reviews. Open access, CC BY 4.0.
Free tools for your next paper
Related gaps in Computer Science
- Finally, we identify gaps in the knowledge of sex differences in athletic performance and the underlying mechanisms, providing substantial opportunities for high-impact studies.Finally, we identify gaps in the knowledge of sex differences in athletic performance and the underlying mechanisms, providing substantial o…
- For verbal working memory, these near-transfer effects were not sustained at follow-up, whereas for visuospatial working memory, limited evidence suggested that such effects might be maintained.For verbal working memory, these near-transfer effects were not sustained at follow-up, whereas for visuospatial working memory, limited evi…
- Although large language models often produce impressive outputs, it remains unclear how they perform in real-world scenarios requiring strong reasoning skills and expert domain knowledge.Although large language models often produce impressive outputs, it remains unclear how they perform in real-world scenarios requiring stron…
- In deep learning (DL), the deep generative model is helpful for data augmentation objectives to tackle the lack of datasets that have a significant impact on learning performance.In deep learning (DL), the deep generative model is helpful for data augmentation objectives to tackle the lack of datasets that have a sign…