XNNTab -- Interpretable Neural Networks for Tabular Data using Sparse Autoencoders

In data-driven applications relying on tabular data, where interpretability is key, machine learning models such as decision trees and linear regression are applied. Although neural networks can provide higher predictive performance, they are not used because of their blackbox nature. In this work, we present XNNTab, a neural architecture that combines the expressiveness of neural networks and interpretability. XNNTab first learns highly non-linear feature representations, which are decomposed into monosemantic features using a sparse autoencoder (SAE). These features are then assigned human-interpretable concepts, making the overall model prediction intrinsically interpretable. XNNTab outperforms interpretable predictive models, and achieves comparable performance to its non-interpretable counterparts.

Towards Interpretable Deep Neural Networks for Tabular Data

Tabular data is the foundation of many applications in fields such as finance and healthcare. Although DNNs tailored for tabular data achieve competitive predictive performance, they are blackboxes with little interpretability. We introduce XNNTab, a neural architecture that uses a sparse autoencoder (SAE) to learn a dictionary of monosemantic features within the latent space used for prediction. Using an automated method, we assign human-interpretable semantics to these features. This allows us to represent predictions as linear combinations of semantically meaningful components. Empirical evaluations demonstrate that XNNTab attains performance on par with or exceeding that of state-of-the-art, black-box neural models and classical machine learning approaches while being fully interpretable.

This looks like what? Challenges and Future Research Directions for Part-Prototype Models

The growing interest in eXplainable Artificial Intelligence (XAI) has prompted research into models with built-in interpretability, the most prominent of which are part-prototype models. Part-Prototype Models (PPMs) make decisions by comparing an input image to a set of learned prototypes, providing human-understandable explanations in the form of ``this looks like that''. Despite their inherent interpretability, PPMS are not yet considered a valuable alternative to post-hoc models. In this survey, we investigate the reasons for this and provide directions for future research. We analyze papers from 2019 to 2024, and derive a taxonomy of the challenges that current PPMS face. Our analysis shows that the open challenges are quite diverse. The main concern is the quality and quantity of prototypes. Other concerns are the lack of generalization to a variety of tasks and contexts, and general methodological issues, including non-standardized evaluation. We provide ideas for future research in five broad directions: improving predictive performance, developing novel architectures grounded in theory, establishing frameworks for human-AI collaboration, aligning models with humans, and establishing metrics and benchmarks for evaluation. We hope that this survey will stimulate research and promote intrinsically interpretable models for application domains. Our list of surveyed papers is available at https://github.com/aix-group/ppm-survey.