The role of AI in U.S. consumer privacy: Developing new concepts for CCPA and GLBA compliance in smart services
Bibliographic record
Abstract
The rapid adoption of artificial intelligence (AI) in U.S. consumer services has transformed customer interactions, operational efficiency, and service delivery. However, this technological shift presents complex challenges in maintaining compliance with data privacy regulations, such as the California Consumer Privacy Act (CCPA) and the Gramm-Leach-Bliley Act (GLBA). This paper explores the role of AI in enhancing smart services while safeguarding consumer privacy, highlighting key risks, compliance challenges, and regulatory gaps. A conceptual model is proposed to guide organizations in integrating privacy-by-design strategies, emphasizing transparency, consent management, and ethical AI principles. The paper also discusses emerging technologies and best practices that support privacy protection while leveraging AI-driven insights. Collaborative efforts between regulators and technology providers are recommended to foster innovation while ensuring robust data privacy. The findings provide practical strategies for balancing technological advancement with regulatory compliance, offering insights for policymakers, industry stakeholders, and service providers. Keywords: Artificial Intelligence, Consumer Privacy, Data Protection, Compliance Strategies, Privacy Regulations, Smart Services.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.048 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".