Possibility based approach improves uncertainty measurement in ai decisions
Possibility-based Inference: A Deterministic Framework for Robust and Reliable Uncertainty Quantification in Decision-Making
Machine Learning
Summary
Making decisions when you don't know everything is really hard, especially for AI in important areas like healthcare or self-driving cars. The paper's authors suggest a new way to measure uncertainty that is more flexible than traditional methods. Instead of only using probabilities, they use something called possibility theory to better handle unclear or conflicting information. This approach helps prevent AI from being too confident about uncertain predictions, making decisions safer and more reliable.
What this means in practice
- •For autonomous vehicle engineers: Improve reliability of AI decisions by measuring uncertainty more flexibly to avoid risky overconfidence in vehicle behavior predictions.
- •For risk assessment teams: Quantify uncertainties in predictive models to better evaluate potential impacts and support safer decision-making under ambiguous data.
Authors
Seyedfakhredin Musavishavazi, Maryam Alipour
Abstract
Decision-making under uncertainty remains a key issue in Machine Learning (ML), particularly in the deployment of safety-critical systems, where reliable quantification of system behavior is essential for evaluating behavioral impacts and strengthening confidence in ML models. This contribution, influenced by fuzzy set theory and grounded in Bayesian statistics, addresses challenges related to data precision and the performance-accuracy trade-off in Bayesian inference (BI). To this end, we introduce a possibility-based contrastive grade of prediction as an alternative quantification method to existing latent posterior-based approaches for parameter estimation (approximation) and model selection. Unlike conventional Bayesian inference, which represents uncertainty within the probabilistic framework, the proposed model adopts a possibility-based representation that allows uncertain and potentially conflicting information to be characterized more flexibly. Rather than focusing on isolated aspects of a problem, the resulting mathematical framework provides a systematic approach to data mining while mitigating overly confident, yet implausible predictions generated by highly specialized deep learning architectures.