Lensing Machines: Representing Perspective in Latent Variable Models

TL;DR

This paper introduces 'lensing', a technique to incorporate human expert perspectives into latent variable models, enabling multiple viewpoints of the same dataset, demonstrated through mental health applications.

Contribution

The paper presents a novel method called lensing for integrating human perspectives into latent variable models, enhancing interpretability and domain relevance.

Findings

Lensing effectively captures expert perspectives in models.

Models with lensing show improved interpretability.

Application to mental health data demonstrates practical benefits.

Abstract

Many datasets represent a combination of different ways of looking at the same data that lead to different generalizations. For example, a corpus with examples generated by different people may be mixtures of many perspectives and can be viewed with different perspectives by others. It isnt always possible to represent the viewpoints by a clean separation, in advance, of examples representing each viewpoint and train a separate model for each viewpoint. We introduce lensing, a mixed initiative technique to extract lenses or mappings between machine learned representations and perspectives of human experts, and to generate lensed models that afford multiple perspectives of the same dataset. We apply lensing for two classes of latent variable models: a mixed membership model, a matrix factorization model in the context of two mental health applications, and we capture and imbue the perspectives of clinical psychologists into these models. Our work shows the benefits of the machine learning practitioner formally incorporating the perspective of a knowledgeable domain expert into their models rather than estimating unlensed models themselves in isolation.