Strengthening Trust and Data Authenticity in AI and ML Through Blockchain-Based Mechanisms
Bibliographic record
Abstract
As we move towards greater penetration of Artificial Intelligence (AI) and Machine Learning (ML) into numerous sectors, the authenticity of data used for training as well as inference is of paramount importance. Integrity, transparency, and robustness of such systems are key in avoiding manipulation and guaranteeing the trustworthiness of the outcome. In this paper, we introduce a new approach, Secure ML, which integrates Blockchain’s decentralized and immutable structure as a security measure to secure and ensure the reliability of the training data in the ML pipeline. We incorporate the Confidential Multi-Party Protocol (CMPP) to ensure the privacy of data and employ the Federated Consensus Protocol (FCP) as the consensus mechanism for secure cooperation. Every dataset is encrypted and validated by smart contracts and made available for verification by any system participants. These verified items are committed to a secure ledger and supported by cloud infrastructure with high redundancy and consistency levels. This architecture is intended to provide a solid basis for trustworthy AI. It addresses how data is shared securely, processed in a verifiable manner, and is less susceptible to adversarial tampering. The architecture considerations provide a relevant framework for real-world implementations and open an avenue for future work on scalable and secure AI deployment in sensitive domains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".