<b> <a href="https://arxiv.org/pdf/2206.00667.pdf">Our paper </a> on explaining the sources of bias in machine learning via influence functions has been accepted in FAccT 2023. </b> Authors: Bishwamittra Ghosh, Debabrota Basu and Kuldeep S. Meel. <br> We combine explainability with fairness in machine learning, where we compute the influence of individual features and the intersectional effect of multiple features on the resulting bias of a classifier on a dataset. This allows us to have a higher granular depiction of sources of bias than earlier methods.