Generative AI systems require management akin to human intelligence sources, not as infallible "oracle machines," to prevent confabulation where AI generates plausible but incorrect inferences. This risk stems from AI's inherent sycophancy, often rewarded for agreeableness over accuracy in training. Effective utilization demands disciplined source selection, evaluating models for specific tasks based on known track records, failure modes, and how they handle uncertainty.
Users must employ elicitation, not interrogation, using indirect, compartmentalized questions to draw out nuanced insights and probe for alternatives. The article warns of "hostile source" analogs in AI, including training data contamination and user automation bias, which leads to over-crediting confident AI outputs. Implementing a debriefing protocol—documenting requirements, prompts, outputs, source checks, human judgment, actions, and reviews—is crucial for calibration and preventing unexamined reporting. While AI excels at collection, human judgment remains vital for analysis and accountability. Organizations must also establish protocols for "burning a source," knowing when to cease reliance on an AI system due to consistent failures or biased outputs.
No comments:
Post a Comment