When evidence expires: Building AI systems that can be audited
The senior AI engineer and researcher examines the career experiences that shaped his approach to document intelligence, computational efficiency and evidence-grounded AI.
Himanshu Goel, a senior AI engineer with extensive experience in predictive modeling, document intelligence, retrieval systems, and AI governance, tackles the challenge of outdated evidence in AI systems. In regulated financial, healthcare, and audit environments, Goel has observed that many failures stem from issues beyond the model itself, such as incorrect document processing, retrieval missing crucial passages, or presenting outdated sources without their history.
To address this, Goel advocates for a holistic approach that encompasses the entire workflow, from information entry to evidence evaluation and user interaction. He stresses the importance of considering how different document passages relate to one another, as conventional retrieval systems that simply find the most similar passages may overlook critical relationships.
Brief written by urgent.news from Jerusalem Post Tech & Start-Ups's own syndicated text. Machine-written — may contain errors; check the original before relying on it.
