AI Health Advice: An Evidence Failure

Google’s AI health advice is under scrutiny not primarily for accuracy, but due to its failure to leave an evidentiary trail. This lack of evidence prevents the reconstruction and inspection of AI-generated outputs, which is crucial in regulated domains where mistakes need to be traceable and correctable. The inability to produce contemporaneous evidence artifacts at the moment of generation poses significant governance challenges, suggesting that AI systems should be treated as audit-relevant entities. This issue raises questions about whether regulators will enforce mandatory reconstruction requirements for AI health information or if platforms will continue to rely on disclaimers and quality assurances. This matters because without the ability to trace and verify AI-generated health advice, accountability and safety in healthcare are compromised.

The issue of AI-generated health advice is not solely about accuracy or safety, but rather about the lack of evidence that accompanies these outputs. When AI systems provide health-related summaries, the inability to trace back what was shown to the user, what claims were made, and what sources were referenced creates a significant challenge. This evidentiary gap means that when the advice is questioned, there is no reliable way to reconstruct the information provided. This absence of a verifiable trail undermines the governance of AI systems in regulated domains, where the ability to inspect and correct mistakes is crucial.

In many industries, such as finance and telecommunications, the introduction of automated decision-making has been accompanied by mandatory record-keeping practices like call recording and audit trails. These measures ensure that decisions can be reviewed and errors addressed. However, in the realm of AI health advice, similar practices have not been established, leading to a situation where the AI’s outputs cannot be reliably scrutinized. This lack of an “evidence artifact” at the moment of information generation means that AI systems are not held to the same standard of accountability as other automated systems.

To address this issue, there is a growing call for AI outputs to be treated as audit-relevant representations. This approach emphasizes capturing the claims, sources, and context of AI-generated advice at the time it is produced, rather than focusing solely on verifying the truthfulness of the information. By doing so, it would be possible to create a more transparent and accountable system where AI-generated health advice can be inspected and validated when necessary. This shift in focus could lead to the development of standards and regulations that mandate the reconstruction of AI outputs in the health sector.

Regulators may need to consider implementing mandatory reconstruction requirements for AI health information to ensure accountability and transparency. Without such measures, platforms may continue to rely on disclaimers and assurances of quality control, which do not address the fundamental issue of evidence. As AI continues to play a larger role in providing health advice, establishing a framework for capturing and auditing AI outputs will be essential to maintain trust and ensure that these systems are governable and reliable. The question remains whether regulators will take this step or if the industry will continue to operate under a less stringent set of guidelines.

Read the original article here