Cross-Jurisdictional data privacy compliance in the U.S.: developing a new model for managing AI data across state and federal laws
Bibliographic record
Abstract
The fragmented landscape of data privacy laws in the United States poses significant challenges for organizations utilizing artificial intelligence (AI) systems that process sensitive and large-scale data. Variations in state laws and the absence of a comprehensive federal framework exacerbate compliance complexities, limiting AI innovation and creating legal uncertainties. This paper proposes a conceptual model to harmonize privacy compliance across U.S. jurisdictions, integrating key interoperability principles, consistency, transparency, and scalability. The framework emphasizes standardized practices for data classification, consent management, risk assessment, and enforcement mechanisms supported by technological enablers such as privacy-enhancing technologies and AI compliance tools. Through case studies in healthcare, e-commerce, and finance, the paper demonstrates the framework’s practical application and effectiveness in resolving multi-jurisdictional compliance challenges. Actionable recommendations for policymakers, organizations, and AI developers are provided to facilitate implementation alongside future research directions to refine the model and address emerging privacy risks. This study offers a roadmap for navigating the complexities of U.S. privacy laws, promoting trust, accountability, and responsible AI innovation. Keywords: AI data governance, U.S. privacy laws, Cross-jurisdictional compliance, Privacy-enhancing technologies, Data protection framework, Ethical AI.
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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 | 0.042 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".