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Quantitative sociologists frequently use simple linear functional forms to estimate associations among variables. However, there is little guidance on whether such simple functional forms correctly reflect the underlying data-generating process. Incorrect model specification can lead to misspecification bias, and a lack of scrutiny of functional forms fosters interference of researcher degrees of freedom in sociological work. In this article, I propose a framework that uses flexible machine learning (ML) methods to provide an indication of the fit potential in a dataset containing the exact same covariates as a researcher's hypothesized model. When this ML-based fit potential strongly outperforms the researcher's self-hypothesized functional form, it implies a lack of complexity in the latter. Advances in the field of explainable AI, like the increasingly popular Shapley values, can be used to generate understanding into the ML model such that the researcher's original functional form can be improved accordingly. The proposed framework aims to use ML beyond solely predictive questions, helping sociologists exploit the potential of ML to identify intricate patterns in data to specify better-fitting, interpretable models. I illustrate the proposed framework using a simulation and real-world examples. [ABSTRACT FROM AUTHOR]

In recent years, natural language processing (NLP) technology has made great progress. Models based on transformers have performed well in various natural language processing problems. However, a natural language task can be carried out by multiple different models with slightly different architectures, such as different numbers of layers and attention heads. In addition to quantitative indicators such as the basis for selecting models, many users also consider the language understanding ability of the model and the computing resources it requires. However, comparing and deeply analyzing two transformer-based models with different numbers of layers and attention heads are not easy because it lacks the inherent one-to-one match between models, so comparing models with different architectures is a crucial and challenging task when users train, select, or improve models for their NLP tasks. In this paper, we develop a visual analysis system to help machine learning experts deeply interpret and compare the pros and cons of asymmetric transformer-based models when the models are applied to a user's target NLP task. We propose metrics to evaluate the similarity between layers or attention heads to help users to identify valuable layers and attention head combinations to compare. Our visual tool provides an interactive overview-to-detail framework for users to explore when and why models behave differently. In the use cases, users use our visual tool to find out and explain why a large model does not significantly outperform a small model and understand the linguistic features captured by layers and attention heads. The use cases and user feedback show that our tool can help people gain insight and facilitate model comparison tasks. [ABSTRACT FROM AUTHOR]

Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with Layer-wise Relevance Propagation (LRP) to detect which brain features contribute to BA. Trained on magnetic resonance imaging (MRI) data of a population-based study (n = 2637, 18–82 years), our models estimated age accurately based on single and multiple modalities, regionally restricted and whole-brain images (mean absolute errors 3.37–3.86 years). We find that BA estimates capture ageing at both small and large-scale changes, revealing gross enlargements of ventricles and subarachnoid spaces, as well as white matter lesions, and atrophies that appear throughout the brain. Divergence from expected ageing reflected cardiovascular risk factors and accelerated ageing was more pronounced in the frontal lobe. Applying LRP, our study demonstrates how superior deep learning models detect brain-ageing in healthy and at-risk individuals throughout adulthood.

In recent years, natural language processing (NLP) technology has made great progress. Models based on transformers have performed well in various natural language processing problems. However, a natural language task can be carried out by multiple different models with slightly different architectures, such as different numbers of layers and attention heads. In addition to quantitative indicators such as the basis for selecting models, many users also consider the language understanding ability of the model and the computing resources it requires. However, comparing and deeply analyzing two transformer-based models with different numbers of layers and attention heads are not easy because it lacks the inherent one-to-one match between models, so comparing models with different architectures is a crucial and challenging task when users train, select, or improve models for their NLP tasks. In this paper, we develop a visual analysis system to help machine learning experts deeply interpret and compare the pros and cons of asymmetric transformer-based models when the models are applied to a user’s target NLP task. We propose metrics to evaluate the similarity between layers or attention heads to help users to identify valuable layers and attention head combinations to compare. Our visual tool provides an interactive overview-to-detail framework for users to explore when and why models behave differently. In the use cases, users use our visual tool to find out and explain why a large model does not significantly outperform a small model and understand the linguistic features captured by layers and attention heads. The use cases and user feedback show that our tool can help people gain insight and facilitate model comparison tasks.