Paper presentation at DRUID25, University of Toronto

On June 26, Tom Dudda presented a recent working paper on „The Perks and Perils of Machine Learning in Business and Economic Research“ (joint work with Lars Hornuf) at the DRUID25 conference at the Rotman School of Management, University of Toronto.

In their paper, Tom Dudda and Lars Hornuf screen over 50k academic publications in leading business and economic journals for articles related to machine learning. They find that predictive machine learning studies are often intransparent about the relative predictive performance compared to less-complex and less-costly traditional statistical models. These studies receive fewer citations, arguably due to a less rigorous analysis.

Because of opaque reporting practices, it’s often unclear whether the predictive gains of more complex models justify their increased costs. To better evaluate their true economic value, the paper advocates for standardized and transparent reporting that relates a model’s predictive performance to its costs—financially, environmentally, and in terms of a loss in explainability and interpretability.

You can access the working paper here: https://www.econstor.eu/handle/10419/314760