Gasser, TheaTheaGasserKlein, EduardEduardKleinSeppänen, LasseLasseSeppänen2024-11-192024-11-192020-10-18978-1-61208-829-72308-349210.24451/arbor.13189https://doi.org/10.24451/arbor.13189https://arbor.bfh.ch/handle/arbor/41781Bias 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). An extensive literature review on the identification and mitigation of bias leads to precise measures for project teams building AI-systems. Aspects like AI-responsibility, AI-fairness and AI-safety are addressed by developing a framework that can be used as a guideline for project teams. It proposes measures in the form of checklists to identify and mitigate bias in algorithmic systems considering all steps during system design, implementation and application.enbiasalgorithmartificial intelligeinceai-safetyalgorithmic systemQA75QA76conference_item