The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

TL;DR

This paper reviews current visualization techniques aimed at increasing trust in machine learning models, categorizing methods, analyzing research trends, and providing insights for future developments in the field.

Contribution

It offers a comprehensive state-of-the-art overview, introduces an improved categorization of trust-related visualization techniques, and provides analytical perspectives supported by an interactive survey tool.

Findings

Categorization of trust in interactive ML models

Statistical overview of research trends

Identification of datasets used in trust visualization studies

Abstract

Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web-based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.