Mitigating Algorithmic Bias in Healthcare AI

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School of Business | Bachelor's thesis
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Date

2025

Department

Tieto- ja palvelujohtamisen laitos

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Mcode

Degree programme

Tieto- ja palvelujohtaminen

Language

en

Pages

32

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Abstract

The integration of artificial intelligence (AI) into healthcare systems has great potential for improving patient outcomes and optimizing services. However, AI algorithms have various risks. A notable one is algorithmic bias, which risks ex-acerbating existing inequities for readily vulnerable populations. This thesis explores the multifaceted challenge of algorithmic bias in healthcare AI systems by analyzing its causes and mitigation strategies. Popula-tions most at risk are studied, with the mitigation strategies focusing on their lived experiences. While having inclusive and representative data is vital, it alone cannot erase algorithmic bias. Therefore, technical approaches, governance structures, and including human decision-makers in the process are necessary for the adoption of equitable AI. Ultimately, this thesis contributes to shaping policies and design principles that prioritize equity and accountability in healthcare AI.

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Thesis advisor

Movarrei, Reza

Keywords

algorithmic bias, healthcare, artificial intelligence, equitable AI, fairness metrics, machine learning bias, equity in healthcare, data governance

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https://urn.fi/URN:NBN:fi:aalto-202510268579

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[kand] Kauppakorkeakoulu / BIZ