Tensor-Based Baum–Welch Algorithms in Coupled Hidden Markov Model for Responsible Activity Prediction
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
The development and applications of artificial intelligence (AI) have brought unprecedented opportunities to humans, but also brought many challenges and concerns such as unfairness, immorality, distrust, illegality, and discrimination. Responsible AI provides a new solution to effectively address these AI potential threats by integrating social/physical rules into AI systems. However, these rules are high-level regulations and ethical principles, which are difficult to be formalized. To this end, we attempt to use the data generated in various AI systems such as cyber–physical–social systems (CPSS) to discover and reflect these rules to provide more responsible services for humans. In this article, we first propose a data-driven responsible CPSS framework. Its core idea is to mine valuable rules through perception, fusion, processing, and analysis of CPSS data, and then use these rules to adaptively optimize CPSS. Based on this framework, three tensor-based couple hidden Markov models (T-CHMMs) are constructed to integrate three responsible features (i.e., timing, periodicity, and correlation) for mining potential and valuable rules. Then, the corresponding tensor-based Baum–Welch (TBW) algorithms are designed to solve their learning problems. Finally, the predictive accuracy and computational efficiency of the proposed models and algorithms are verified on three open datasets. The experimental results show that proposed methods have the best performances for various scenarios, which reflects that our methods are more promising and responsible than existing methods.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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