Ethical Guardrails Are Essential To Making Generative AI Work For Healthcare

A specialist provides his thoughts on a "clean data" approach and explains why explicit agreement is necessary for training algorithms, adding that data integrity and model validity are important requirements.

The subject of guardrails is a hot one in the generative AI community. Since there are few government regulations, it is up to corporations and authorities to establish a secure environment where consumers can interact with generative AI. Data "hallucinations," or errors in data, are one of the most significant issues to date. Users with insufficient experience who overly rely on generative AI techniques expose these serious problems.

Edwin Pahk, vice president of growth at AI-powered service provider Aquant, believes that generative AI must prioritize the source's collaboration just as he conducts his business with a "clean data" approach.