Handling Epistemic and Aleatory Uncertainties in Probabilistic Circuits
This addresses the need for reliable confidence assessments in AI systems to prevent catastrophic failures, representing an incremental improvement in handling uncertainties for probabilistic models.
The paper tackles the problem of accurately estimating epistemic uncertainty in probabilistic reasoning to improve trust in AI recommendations, achieving better uncertainty estimation than state-of-the-art approaches with only a modest computational increase.
When collaborating with an AI system, we need to assess when to trust its recommendations. If we mistakenly trust it in regions where it is likely to err, catastrophic failures may occur, hence the need for Bayesian approaches for probabilistic reasoning in order to determine the confidence (or epistemic uncertainty) in the probabilities in light of the training data. We propose an approach to overcome the independence assumption behind most of the approaches dealing with a large class of probabilistic reasoning that includes Bayesian networks as well as several instances of probabilistic logic. We provide an algorithm for Bayesian learning from sparse, albeit complete, observations, and for deriving inferences and their confidences keeping track of the dependencies between variables when they are manipulated within the unifying computational formalism provided by probabilistic circuits. Each leaf of such circuits is labelled with a beta-distributed random variable that provides us with an elegant framework for representing uncertain probabilities. We achieve better estimation of epistemic uncertainty than state-of-the-art approaches, including highly engineered ones, while being able to handle general circuits and with just a modest increase in the computational effort compared to using point probabilities.