TECH & AI
What strategies are you considering to mitigate the risks of model bias and hallucination when deploying LLMs in critical financial processes?
Richard Doherty, Wealth & Asset Management Leader at Publicis Sapient Bias and hallucinations are manageable, but only with the right strategies. This means curated training data, continuous testing in productionlike environments, human-in-the-loop validation, and architecture choices that prioritise factual grounding.
Leading institutions are implementing layered safeguards: combining internal knowledge bases, usage guardrails and realtime monitoring to prevent off-policy behaviour in sensitive contexts.
You need protection at multiple levels, at the data level through diverse, representative training sets; at the architecture level through design choices that prioritise accuracy over fluency; and at the operational level through continuous validation and monitoring.
The most effective approach involves systematic safeguards that operate throughout the AI system, not just at the endpoints. This creates multiple checkpoints that catch potential issues before they affect critical business decisions.
Richard Harmon, Vice President and Global Head of Financial Services at Red Hat A comprehensive suite of tools is essential for rigorously testing models throughout their entire lifecycle, from initial data sourcing and algorithm development to output evaluation, explanation and utilisation.
In essence, what we now term“ hallucinations” are, in my view, what we have always had in all types of models- i. e., model error!
As with other areas, open source within the AI context helps to drive innovation and safety through thousands of community-supported projects. Leveraging global opensource community efforts is crucial.
These communities offer a vast array of collaboratively developed tools, extending beyond the limited scope of a single firm’ s developers.
Enterprises must develop and implement a comprehensive AI platform that mandates rigour and consistency, alongside stringent ethical and privacy standards, irrespective of the user, the algorithms, or the use case.
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