Pith. sign in

REVIEW 1 cited by

Navigating the United States Legislative Landscape on Voice Privacy: Existing Laws, Proposed Bills, Protection for Children, and Synthetic Data for AI

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.19677 v1 pith:V6TOGT46 submitted 2024-07-29 cs.CY cs.CRcs.SDeess.AS

classification cs.CYcs.CRcs.SDeess.AS
keywords dataprivacylawslegislationsyntheticchildrenpolicymakersprotection
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Privacy is a hot topic for policymakers across the globe, including the United States. Evolving advances in AI and emerging concerns about the misuse of personal data have pushed policymakers to draft legislation on trustworthy AI and privacy protection for its citizens. This paper presents the state of the privacy legislation at the U.S. Congress and outlines how voice data is considered as part of the legislation definition. This paper also reviews additional privacy protection for children. This paper presents a holistic review of enacted and proposed privacy laws, and consideration for voice data, including guidelines for processing children's data, in those laws across the fifty U.S. states. As a groundbreaking alternative to actual human data, ethically generated synthetic data allows much flexibility to keep AI innovation in progress. Given the consideration of synthetic data in AI legislation by policymakers to be relatively new, as compared to that of privacy laws, this paper reviews regulatory considerations for synthetic data.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FeatureSense: Protecting Speaker Attributes in Always-On Audio Sensing System

    cs.SD 2025-05 conditional novelty 6.0 of 10

    FeatureSense exposes hand-picked audio features instead of raw audio and introduces the SILI metric, claiming 60.6% lower speaker attribute leakage while keeping sound classification accuracy.

Pith tools