Use Conformal Prediction to Quantify AI Uncertainty

In decision-making, Artificial Intelligence (AI) systems need to not only make predictions but also quantify their predictions' certainty (uncertainty). For example, consider a stock automatic trading system where an AI system predicts the stock price. A point prediction from the AI system might be dramatically different from the real value because of the high stochasticity of the stock market. But, on the other hand, if the AI system could estimate the range which guarantees to cover the true value with high probability, the trading system could compute the best and worst rewards and make more sensible decisions. Conformal prediction is a technique for quantifying such uncertainties for AI systems.