@article{NIK2020-b, abstract = {The use of artificial intelligence for decision making raises concerns about the societal impact of such systems. Traditionally, the product of a human decision-maker are governed by laws and human values. Decision-making is now being guided - or in some cases, replaced by machine learning classification which may reinforce and introduce bias. Algorithmic bias mitigation is explored as an approach to avoid this, however it does come at a cost: efficiency and accuracy. We conduct an empirical analysis of two off-the-shelf bias mitigation techniques from the AIF360 toolkit on a binary classification task. Our preliminary results indicate that bias mitigation is a feasible approach to ensuring group fairness.}, author = {Tor H. Aasheim and Knut T. Hufthammer and S{\o}lve {\AA}nneland and H{\aa}avard Brynjulfsen and Marija Slavkovik}, journal = {Norsk Informatikkonferanse}, note = {Awarded Best Paper}, title = {Bias mitigation with AIF360: A comparative study}, url = {https://ojs.bibsys.no/index.php/NIK/article/view/833}, year = {2020}, bdsk-url-1 = {https://ojs.bibsys.no/index.php/NIK/article/view/833}}