Consistent Estimators for Learning to Defer to an Expert (2006.01862v3)

Abstract: Learning algorithms are often used in conjunction with expert decision makers in practical scenarios, however this fact is largely ignored when designing these algorithms. In this paper we explore how to learn predictors that can either predict or choose to defer the decision to a downstream expert. Given only samples of the expert's decisions, we give a procedure based on learning a classifier and a rejector and analyze it theoretically. Our approach is based on a novel reduction to cost sensitive learning where we give a consistent surrogate loss for cost sensitive learning that generalizes the cross entropy loss. We show the effectiveness of our approach on a variety of experimental tasks.

• The paper introduces a convex surrogate loss (L_CE) that ensures consistency in deciding when to predict or defer to an expert.
• It integrates both classifier and expert costs into a unified system loss to establish optimal decision boundaries.
• Experimental results on CIFAR datasets and hate speech detection demonstrate enhanced sample efficiency compared to traditional methods.

Consistent Estimators for Learning to Defer to an Expert: A Summary

The paper presented by Mozannar and Sontag addresses the computational and theoretical challenges associated with integrating learning models that can autonomously decide whether to predict or defer to an expert. This situation is prevalent in various applied domains such as healthcare and content moderation, where models potentially complement human decision-makers. The authors propose a formal methodology for learning predictors that can assertively choose between making a prediction or deferring the decision to a downstream expert.

Theoretical Contributions

The authors define the setup as a multiclass learning problem, expanding the previous works in rejection learning by including expert deferral and constructing a system loss function. This system loss function considers two components: the classifier cost and the expert cost, each uniquely influencing the decision boundary of when a model should defer to an expert versus making an independent prediction.

A significant contribution of this paper is the derivation of a novel convex surrogate loss, $L_{CE}$, which manages the conversion from the expert deferral setup to a cost-sensitive learning framework. Importantly, this surrogate loss maintains consistency — meaning, it is proven to converge to the Bayes optimal solution under certain conditions, which offers validity to its application in the practical scenario.

Experimental Evaluation

An array of experimental tasks, including image classification with CIFAR-10 and CIFAR100 datasets as well as hate speech detection with text data, showcase the efficacy of their method. The experiments compare traditional confidence-based methods with their proposed surrogate loss, $L_{CE}^\alpha$, and demonstrate superior adaptability to the model-expert boundary, enabling adjusted coverage based on the model's confidence.

One key advantage observed in their experiments is the sample complexity — constrained datasets limit traditional methods, while the joint-learned rejector and classifier using $L_{CE}$ are shown to effectively adapt and outperform existing baselines.

Implications and Future Directions

This work lays a foundation for the understanding of model-expert integration in machine learning systems, offering a theoretical and practical framework for application. While the algorithm shows promise in scenarios where full automation isn't feasible, it also opens a speculative avenue for future research to explore personalized deferral decisions in complex multi-expert environments.

Furthermore, the implications of this approach extend beyond improved prediction accuracy and apply towards computational efficacy, ethical AI deployment (e.g., reducing bias through expert deferral), and dynamic learning systems that evolve with expert behavior.

Conclusion

Ultimately, this paper's contribution to the AI community is rather substantial in formalizing the methodology for learning models that complement experts, providing both theoretical solidity and empirical evidence for the benefits of such a system. Future developments can enrich the scope of this model by incorporating further real-world complexities, such as fairness constraints or multi-expert settings, driving towards a nuanced symbiosis of AI and human expertise.