Marginal Alignment Does Not Guarantee Joint-Distribution Fidelity: An Official-Reference Audit of Nemotron-Personas-Korea with Cross-Locale Replication
arXiv:2606.12433v1 Announce Type: cross Abstract: Synthetic persona datasets cite alignment with official demographics as a basis for trust, yet downstream users consume them as joint structures across age, sex, region, occupation, education, name, and institutional status. Marginal alignment does not imply that these joints are preserved. We propose the Independence-Assumption Footprint (IAF), an audit primitive that operates on the attribute combinations a dataset card itself documents as treated independently. For each such combination, IAF compares the synthetic joint against an external o
The proliferation of synthetic datasets and their use in AI model training necessitates rigorous auditing standards to ensure fidelity and mitigate risks.
This research provides a critical methodology for evaluating the representational accuracy of synthetic datasets, directly impacting the fairness and reliability of AI systems built upon them.
The proposed 'Independence-Assumption Footprint' introduces a new audit primitive for assessing joint-distribution fidelity in synthetic persona datasets, challenging the superficial trust placed in marginal alignment.
- · AI ethics researchers
- · AI auditing firms
- · Developers of robust synthetic data generation methods
- · Developers relying solely on marginal demographic alignment
- · Companies with inadequately audited synthetic datasets
- · Users of biased synthetic datasets
Increased scrutiny and demand for more sophisticated auditing of synthetic datasets across the AI industry.
The development of new tools and benchmarks for joint-distribution fidelity, potentially driving innovation in synthetic data generation and validation.
A potential shift in regulatory emphasis from just privacy preservation to encompassing representational accuracy in AI training data, influencing future AI governance frameworks.
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Read at arXiv cs.CL