{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:14:52Z","timestamp":1758672892420,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Explainability and uncertainty quantification are key to trustable artificial intelligence. However, the reasoning behind uncertainty estimates is generally left unexplained. Identifying the drivers of uncertainty complements explanations of point predictions in recognizing model limitations and enhancing transparent decision-making. So far, explanations of uncertainties have been rarely studied. The few exceptions rely on Bayesian neural networks or technically intricate approaches, such as auxiliary generative models, thereby hindering their broad adoption. We propose a straightforward approach to explain predictive aleatoric uncertainties. We estimate uncertainty in regression as predictive variance by adapting a neural network with a Gaussian output distribution. Subsequently, we apply out-of-the-box explainers to the model's variance output. This approach can explain uncertainty influences more reliably than complex published approaches, which we demonstrate in a synthetic setting with a known data-generating process. We substantiate our findings with a nuanced, quantitative benchmark including synthetic and real, tabular and image datasets. For this, we adapt metrics from conventional XAI research to uncertainty explanations. Overall, the proposed method explains uncertainty estimates with little modifications to the model architecture and outperforms more intricate methods in most settings.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/607","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"5453-5462","source":"Crossref","is-referenced-by-count":0,"title":["Identifying Drivers of Predictive Aleatoric Uncertainty"],"prefix":"10.24963","author":[{"given":"Pascal","family":"Iversen","sequence":"first","affiliation":[{"name":"Freie Universit\u00e4t Berlin, Department of Mathematics and Computer Science, Berlin, Germany"},{"name":"Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Germany"}]},{"given":"Simon","family":"Witzke","sequence":"additional","affiliation":[{"name":"Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Germany"}]},{"given":"Katharina","family":"Baum","sequence":"additional","affiliation":[{"name":"Freie Universit\u00e4t Berlin, Department of Mathematics and Computer Science, Berlin, Germany"},{"name":"Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Germany"},{"name":"Windreich Department of Artificial Intelligence and Human Health & Hasso Plattner Institute for Digital Health at Mount Sinai"}]},{"given":"Bernhard Y.","family":"Renard","sequence":"additional","affiliation":[{"name":"Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Germany"},{"name":"Freie Universit\u00e4t Berlin, Department of Mathematics and Computer Science, Berlin, Germany"},{"name":"Windreich Department of Artificial Intelligence and Human Health & Hasso Plattner Institute for Digital Health at Mount Sinai"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:34:35Z","timestamp":1758627275000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/607"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/607","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}