Work

Bias mitigation with AIF360: A comparative study

Year: 2020

Type: book-chapter

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.

Source: NORA - Norwegian Open Research Archives

Authors: Knut T. Hufthammer, Tor H. Aasheim, Sølve Ånneland, Håvard Brynjulfsen, Marija Slavkovik

Institution: University of Bergen

Language: Norwegian

Topic: Ethics and Social Impacts of AI

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3157616621

FWCI: 1.261

Cites: 5

Cited by: 8

Related to: 10