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Bias in Artificial Intelligence: Exploring Its Role in Institutional Discrimination and Strategies for Mitigation
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Orientador(es)
Resumo(s)
Artificial Intelligence, especially through machine learning, has become ubiquitous in
everyday life, influencing both individual choices and institutional decision-making
processes. Despite its potential to bring societal benefits, AI has faced considerable criticism
because it perpetuates biases contributing to institutional discrimination. This thesis explores
how biases come into AI systems through imbalance in data, algorithmic design, or user
interaction, and how they reinforce systemic inequalities in critical domains such as
recruitment, justice, and finance. The thesis is based on an extensive literature review,
discussing both the technical foundations of AI and the notion of institutional discrimination
defined under the EU and German frameworks as systemic inequalities that are buried
within institutional practices. It showcases real-world impacts: the biased algorithms,
including Amazon's hiring tool, the COMPAS criminal justice system, and discriminatory
credit scoring mechanisms, through which AI systems replicate and amplify historical
inequities. These challenges are addressed by this research, which assesses the strategies for
mitigating AI bias through technical, regulatory, and ethical approaches. Technical
interventions include fairness-aware algorithms and improved data practices, while
regulatory measures, such as the EU AI Act and GDPR, enforce accountability and
transparency. Ethical frameworks, including the OECD Principles on AI and EU Ethics
Guidelines for Trustworthy AI, emphasize inclusivity and fairness in AI governance. By
integrating these perspectives, actionable strategies to reduce bias and advance equity in AI
systems are provided. It reflects a deep understanding of the power of interdisciplinary
collaboration in AI for social progress, fairness, and accountability in its deployment
processes.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Artificial Intelligence Machine Learning Bias Discrimination Mitigation SDG 9 - Industry, innovation and infrastructure SDG 10 - Reduced inequalities SDG 16 - Peace, justice and strong institutions