Home // International Journal On Advances in Software, volume 14, numbers 1 and 2, 2021 // View article

Validation of a Framework for Bias Identification and Mitigation in Algorithmic Systems

Authors:
Thea Gasser
Rémy Bohler
Eduard Klein
Lasse Seppänen

Keywords: Bias Framework; Artificial intelligence; Algorithmic system; Validation.

Abstract:
Bias in algorithmic systems is a major cause of unfair and discriminatory decisions in the use of such systems. Cognitive bias is very likely to be reflected in algorithmic systems as humankind aims to map Human Intelligence (HI) to Artificial Intelligence (AI). We conducted an extensive literature review on the identification and mitigation of bias, leading to precise measures for project teams building AI systems. Moreover, we developed an awareness-raising framework for use as a guideline for project teams, addressing AI responsibility, AI fairness, and AI safety. The framework proposes measures in the form of checklists to identify and mitigate bias in algorithmic systems considering all steps during system design, implementation, and application. We validated the framework successfully in the context of industrial AI projects.

Pages: 59 to 70

Copyright: Copyright (c) to authors, 2021. Used with permission.

Publication date: December 31, 2021

Published in: journal

ISSN: 1942-2628