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4.2. Structuring Data for Control, Compliance, and Clarity?

Structuring Data for Control, Compliance, and Clarity?

In several studies revealed that AI-powered medical chatbots, despite their fluency, were producing dangerously misleading outputs. One analysis published in The American Journal of Medicine found that nearly 46% of citations generated by ChatGPT in medical responses were fabricated, and over 27% of the content was hallucinated1. Another study discovered that models suggested off-label drug use, invented treatment protocols, and offered advice without grounding in peer-reviewed evidence2.

The problem was not model design, it was data governance.

These systems had been trained on web-scraped forums, clinical texts, and patient posts without consistent metadata, licensing records, or quality tracking. Developers could not trace where a hallucinated answer came from, whether it was informed by expert material, or if it originated from outdated, biased, or even dangerous user-generated content.

The failure was foundational:

  • No data lineage
  • No annotation versioning
  • No consent or usage tracking
  • No audit trail

And once the model was deployed, it was too late to correct. Users were already exposed to misleading or unsafe information, without transparency, accountability, or remediation.

These incidents highlight a broader truth across AI systems:

Governance doesn’t begin when a model is deployed.
It begins the moment data is collected, and if that moment is missed, no downstream audit can fix what was never recorded.

Poorly structured data pipelines leave developers unable to answer critical questions: - Who collected the data, and under what consent? - What was added, removed, or re-labeled during preprocessing? - Can specific examples be traced and removed if found to be harmful?

In high-risk domains like healthcare, law, and finance, this lack of visibility doesn’t just delay progress, it puts people at risk.

To build trustworthy AI, organizations must embed governance upstream, at the data level. This requires designing pipelines that are:

  • Traceable: with clear lineage and metadata
  • Accountable: with assigned roles and responsibilities
  • Controllable: with mechanisms to revise, monitor, and correct data use over time

In the next section, we explore how the standard addresses these needs by transforming datasets from opaque collections into structured, governable, and auditable assets, fit for deployment in complex, real-world systems.

Bibliography

  1. Bhattacharyya, M., Miller, V. M., Bhattacharyya, D., & Miller, L. E. (2023, May 19). High Rates of Fabricated and Inaccurate References in ChatGPT-Generated Medical Content. Cureus, 15(5), e39238. https://doi.org/10.7759/cureus.39238 ↩

  2. Ji, Z., Lee, N., Frieske, R., et al. (2023). Survey of Hallucination in Natural Language Generation. arXiv preprint arXiv:2301.13823. https://arxiv.org/abs/2301.13823 ↩