Uncertainty Estimation by Human Perception versus Neural Models (2506.15850v1)

Abstract: Modern neural networks (NNs) often achieve high predictive accuracy but remain poorly calibrated, producing overconfident predictions even when wrong. This miscalibration poses serious challenges in applications where reliable uncertainty estimates are critical. In this work, we investigate how human perceptual uncertainty compares to uncertainty estimated by NNs. Using three vision benchmarks annotated with both human disagreement and crowdsourced confidence, we assess the correlation between model-predicted uncertainty and human-perceived uncertainty. Our results show that current methods only weakly align with human intuition, with correlations varying significantly across tasks and uncertainty metrics. Notably, we find that incorporating human-derived soft labels into the training process can improve calibration without compromising accuracy. These findings reveal a persistent gap between model and human uncertainty and highlight the potential of leveraging human insights to guide the development of more trustworthy AI systems.

Uncertainty Estimation by Human Perception versus Neural Models: An Expert Analysis

The paper "Uncertainty Estimation by Human Perception versus Neural Models" scrutinizes the disparities between neural network (NN) predictions and human intuition concerning uncertainty estimation. The authors critically assess how well NN-derived uncertainty aligns with human-perceived uncertainty and explore the possibility of integrating human insights to enhance model calibration. Given the increasing reliance on models in high-stakes applications, recognizing discrepancies between human and model uncertainty assessments is pertinent for fostering trustworthy AI systems.

Calibration in Neural Networks

Modern NNs exhibit high predictive accuracy across various tasks yet often produce overconfident predictions, a phenomenon known as poor calibration. This poses significant problems in critical applications where reliable uncertainty estimates are crucial. A range of methods such as Bayesian Neural Networks (BNNs), Monte Carlo (MC) Dropout, and post-hoc calibration techniques like Isotonic Regression and Temperature Scaling have been developed to address these calibration issues. However, these approaches primarily focus on statistical measures and do not adequately reflect human perceptions of uncertainty.

Research Objectives and Findings

The research focuses on the extent to which model uncertainty estimates reflect human perceptual uncertainty. Using three vision benchmarks enriched with human annotations, the paper systematically compares human and model uncertainty across diverse tasks, employing prediction entropy as a primary metric. The investigation reveals a weak alignment between model and human uncertainty assessments, with Pearson's correlation coefficients being notably low and statistically insignificant in some cases. This suggests that current model uncertainty estimations fail to fully capture the nuances of human intuitive judgment.

Moreover, incorporating human-derived soft labels into the training process yielded promising results, slightly improving model calibration without sacrificing accuracy. The paper highlights that models trained with human insights showed improved alignment with human intuition, particularly in tasks with ambiguous or noisy inputs. Despite these improvements, the correlation remains suboptimal, underscoring the complexity of modeling human-like uncertainty estimation.

Implications and Future Directions

The paper moves beyond merely improving statistical calibration by emphasizing the crucial gap between human-perceived uncertainty and NN predictions. This gap, persistent across datasets and tasks, challenges the development of AI systems that users can trust. The findings underpin the necessity for hybrid approaches that combine human intuition and machine predictions to enhance model reliability and interpretability.

For future developments, the paper suggests exploring advanced methods to incorporate human-like reasoning patterns into NN training and calibration. This may involve refining current training techniques, enhancing interpretability frameworks, and developing comprehensive measures to encapsulate both epistemic and aleatoric uncertainty within a unified model framework.

Conclusion

In summary, while contemporary NNs demonstrate robust accuracy, their calibration does not yet align with human perceptions of uncertainty. This paper lays the groundwork for future research aimed at developing models that are not only statistically adept but also resonate with human intuition. Such endeavors are essential for ensuring AI's trustworthy integration into domains requiring critical judgment, ultimately leading towards more human-compatible AI systems.