Learning Predictive Checklists with Probabilistic Logic Programming (2411.16790v1)

Abstract: Checklists have been widely recognized as effective tools for completing complex tasks in a systematic manner. Although originally intended for use in procedural tasks, their interpretability and ease of use have led to their adoption for predictive tasks as well, including in clinical settings. However, designing checklists can be challenging, often requiring expert knowledge and manual rule design based on available data. Recent work has attempted to address this issue by using machine learning to automatically generate predictive checklists from data, although these approaches have been limited to Boolean data. We propose a novel method for learning predictive checklists from diverse data modalities, such as images and time series. Our approach relies on probabilistic logic programming, a learning paradigm that enables matching the discrete nature of checklist with continuous-valued data. We propose a regularization technique to tradeoff between the information captured in discrete concepts of continuous data and permit a tunable level of interpretability for the learned checklist concepts. We demonstrate that our method outperforms various explainable machine learning techniques on prediction tasks involving image sequences, time series, and clinical notes.

• The paper introduces ProbChecklist, advancing interpretable machine learning by leveraging probabilistic logic to learn predictive checklists from complex data.
• It employs concept learners to convert continuous data into binary, interpretable features while integrating deep learning techniques.
• Empirical results on healthcare and imaging tasks show enhanced predictive performance and fairness compared to traditional checklist models.

Learning Predictive Checklists with Probabilistic Logic Programming

The paper "Learning predictive checklists with probabilistic logic programming" advances the domain of interpretable machine learning by proposing an innovative method for constructing predictive checklists. This research demonstrates the potential of probabilistic logic programming to generate checklists from diverse data modalities, including complex forms such as images, time series, and textual data. The checklist model's interpretability is situated to bridge the gap between high performance and transparency, particularly in critical areas like healthcare.

Core Concepts and Methodology

The authors introduce ProbChecklist, an architecture that employs probabilistic logic programming to learn predictive checklists. Unlike existing checklist models, which are predominantly restricted to Boolean or categorical inputs, their approach is versatile enough to handle continuous and high-dimensional data types. This versatility is achieved through concept learners that extract binary interpretable concepts from continuous data modalities.

ProbChecklist's methodology is formulated within the probabilistic logic framework, permitting integration with deep learning techniques. Specifically, the paper defines a probabilistic checklist model where the prediction of each sample depends on the probabilistic aggregation of binary concepts learned from its features. The practicality of the framework is enhanced by integrating regularization strategies that maintain the interpretability of these concepts.

Theoretical Implications and Empirical Results

This work is significant in its demonstration of how probabilistic reasoning and machine learning can be elegantly combined through a logical programming paradigm. It underscores an important theoretical contribution by showing that checklists can be learned not only from tabular data but also from more complex data constructs, a challenge previously limiting the application of checklist-based models.

Empirically, the method is validated across multiple datasets, including a synthetic MNIST checklist dataset, the PhysioNet 2019 Early Sepsis Prediction dataset, MIMIC III mortality prediction data, and a neoplasm detection task using clinical notes. The results indicate that ProbChecklist can outperform traditional interpretable approaches such as Integer Linear Program (ILP) and Mixed Integer Programming (MIP), especially in terms of predictive performance on challenging data modalities involving images and text.

Furthermore, the paper includes a fairness regularization experiment that adjusts the prediction model by mitigating bias across sensitive subgroups in the data. The application of fairness constraints led to significant reductions in false positive and negative rate disparities among different demographics, highlighting the practical importance of equitable machine learning practices in healthcare applications.

Future Directions and Implications

The introduction of ProbChecklist sets a new precedent for how interpretable, rule-based models can be devised through advanced machine learning paradigms. The potential extensions of this work are quite expansive. Future research might explore the integration of more complex neural network-based concept learners to refine the quality and relevance of the extracted checklist items. Additionally, there is room for work on optimizing the computational efficiency of training and inference processes, as the current implementation incurs exponential complexity under certain conditions.

Given the increasing reliance on artificial intelligence in sectors demanding transparency, such as healthcare, finance, and law, the broader implications of this work are substantial. Enhanced interpretability not only facilitates regulatory compliance but also builds trust with end users, particularly in applications where decision explanations are critical.

In conclusion, the paper represents a noteworthy advancement in probabilistic logic programming applications within AI, demonstrating how logical structures can underlie robust and interpretable predictive models. This approach could play a pivotal role in overcoming the interpretability challenges faced by black-box machine learning models, particularly in safety-critical areas.