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

Klein, Eduard (2021). Validation of a Framework for Bias Identification and Mitigation in Algorithmic Systems International Journal on Advances in Software, 14(1&2), pp. 59-70. IARIA

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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.

Item Type:	Journal Article (Original Article)
Division/Institute:	Business School > Institute for Public Sector Transformation Business School > Institute for Public Sector Transformation > Data and Infrastructure Business School
Name:	Klein, Eduard0000-0002-6860-5845
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ISSN:	1942-2628
Publisher:	IARIA
Language:	English
Submitter:	Eduard Klein
Date Deposited:	07 Feb 2022 14:15
Last Modified:	30 May 2022 13:42
Related URLs:	Publication
Uncontrolled Keywords:	Bias Framework; Artificial intelligence; Algorithmic system; Validation.
ARBOR DOI:	10.24451/arbor.16560
URI:	https://arbor.bfh.ch/id/eprint/16560