The Fair Universe project organised the HiggsML Uncertainty Challenge. This groundbreaking competition in high-energy physics (HEP) and machine learning was the first to strongly emphasise uncertainties, focusing on mastering both the uncertainties in the input training data and providing credible confidence intervals in the results. The challenge revolved around measuring the $(H \rightarrow \tau^+ \tau^-)$ cross-section, similar to the HiggsML challenge held on Kaggle in 2014, using a dataset representing the 4-momentum signal state. Participants were tasked with developing advanced analysis techniques to measure the signal strength and generate confidence intervals that included both statistical and systematic uncertainties. The accuracy of these intervals was automatically evaluated using pseudo-experiments to assess correct coverage. The dataset is now published in Zenodo, and the winning submissions are fully documented.