What is it about?
Social scientists, particularly psycholgists, are increasingly using machine learning to make predictions based on large and complex data sets. However, these models often work like “black boxes,” making it difficult to understand how decisions are made. This article introduces psychologists to a set of tools known as Explainable Artificial Intelligence (XAI), which can help make sense of these models. It provides a clear guide to commonly used XAI techniques—like SHAP, LIME, and partial dependence plots—and explains how they can be applied in psychological research. The authors also highlight important challenges, such as how related variables (multicollinearity) can confuse results, and demonstrate these issues using a simulation. Overall, the article equips social scientists with the tools and understanding they need to apply machine learning responsibly and effectively in their work.
Featured Image
Photo by Andres Siimon on Unsplash
Why is it important?
This article is timely and important because psychology is undergoing a rapid shift toward data-driven research using machine learning. Yet, most psychologists are not trained to interpret the inner workings of these complex models. By offering an accessible and practical introduction to XAI, this work fills a major knowledge gap. It empowers researchers to not only build accurate models but also understand and communicate their findings clearly—helping to avoid misinterpretations and increase transparency. The guidance offered here is especially critical as machine learning becomes more widely used in psychological assessment, treatment planning, and policy development.
Perspectives
From our perspective, this article is a much-needed bridge between psychology and machine learning. We wrote it with the aim of demystifying XAI, so they can move beyond treating models as black boxes and begin using them as tools for discovery. One of the key takeaways is that there's no single best method for explaining a model—different tools work better in different contexts (and all have their positives and negatives, which we outline). We hope this handbook will not only clarify how to choose and apply XAI methods but also promote more critical and responsible use of AI in psychological research. The open-source code we provide is meant to support hands-on learning and experimentation.
Rosa Lavelle-Hill
University of Basel
Read the Original
This page is a summary of: An explainable artificial intelligence handbook for psychologists: Methods, opportunities, and challenges., Psychological Methods, July 2025, American Psychological Association (APA),
DOI: 10.1037/met0000772.
You can read the full text:
Contributors
The following have contributed to this page
