Cross-Jurisdictional data privacy compliance in the U.S.: developing a new model for managing AI data across state and federal laws
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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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