Bias mitigation with AIF360: A comparative study (bibtex)
@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},
	Title = {Bias mitigation with AIF360: A comparative study},
	Url = {http://slavkovik.com/NIK_2020_paper_13.pdf},
	note={Awarded Best Paper},
	Year = {2020}
	}
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