AI-Augmented Forecasting

AI-augmented forecasting combines the pattern-recognition and data-processing capabilities of AI systems with the contextual judgment and calibration of human forecasters. This hybrid approach aims to produce more accurate predictions about future events than either humans or AI alone, particularly for questions relevant to policy and risk assessment. Current systems take several forms. AI can aggregate and weight forecasts from many human predictors, adjusting for individual track records and biases. AI can assist forecasters by synthesizing relevant information, identifying base rates, and flagging considerations that might otherwise be missed. More ambitiously, AI systems can generate their own forecasts that human superforecasters then evaluate and combine with their own judgments. For AI safety and epistemic security, improved forecasting offers several benefits. Better predictions about AI capabilities help with governance timing. Forecasting AI-related risks provides early warning. Publicly visible forecasts create accountability for claims about AI development. The key challenge is calibration - ensuring that probability estimates are meaningful across diverse domains and maintaining accuracy as AI systems become the subject of the forecasts themselves.