# Assessing Intersectional Bias in Representations of Pre-Trained Image Recognition Models Source: [Assessing Intersectional Bias in Representations of Pre-Trained Image Recognition Models]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:52:59.877Z Authors: Valerie Krug, Sebastian Stober Published: 2025-06-04T07:55:52.000Z ## Abstract Deep Learning models have achieved remarkable success. Training them is often accelerated by building on top of pre-trained models which poses the risk of perpetuating encoded biases. Here, we investigate biases in the representations of commonly used ImageNet classifiers for facial images while considering intersections of sensitive variables age, race and gender. To assess the biases, we use linear classifier probes and visualize activations as topographic maps. We find that representations in ImageNet classifiers particularly allow differentiation between ages. Less strongly pronounced, the models appear to associate certain ethnicities and distinguish genders in middle-aged groups. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()