Explainable AI for Decision-Making: A Hybrid Approach to Trustworthy Computing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the evolving landscape of intelligent systems, ensuring transparency, fairness, and trust in artificial intelligence (AI) decision-making is paramount. This study presents a hybrid Explainable AI (XAI) framework that integrates rule-based models with deep learning techniques to enhance interpretability and trustworthiness in critical computing environments. The proposed system employs Layer-Wise Relevance Propagation (LRP) and SHAP (SHapley Additive exPlanations) for local and global interpretability, respectively, while leveraging a Convolutional Neural Network (CNN) backbone for accurate decision-making across diverse domains, including healthcare, finance, and cybersecurity. The hybrid model achieved an average accuracy of 94.3%, a precision of 91.8%, and an F1-score of 93.6%, while maintaining a computation overhead of only 6.7% compared to standard deep learning models. The trustworthiness index, computed based on interpretability, robustness, and fairness metrics, reached 92.1%, demonstrating significant improvement over traditional black-box models.This work underscores the importance of explainability in AI-driven decision-making and provides a scalable, domain-agnostic solution for trustworthy computing. The results confirm that integrating explainability mechanisms does not compromise performance and can enhance user confidence, regulatory compliance, and ethical AI deployment
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it