@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}}