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Bias in Artificial Intelligence: Exp...

Oberhauser, Hubert Josef,

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Bias in Artificial Intelligence: Exploring its Role in Institutional Discrimination and Strategies for Mitigation /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bias in Artificial Intelligence: Exploring its Role in Institutional Discrimination and Strategies for Mitigation // Hubert Josef Oberhauser.
Author:	Oberhauser, Hubert Josef,
Description:	1 electronic resource (56 pages)
Notes:	Source: Masters Abstracts International, Volume: 86-10.
Contained By:	Masters Abstracts International86-10.
Subject:	Software. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31972464
ISBN:	9798311945899

Bias in Artificial Intelligence: Exploring its Role in Institutional Discrimination and Strategies for Mitigation /
Oberhauser, Hubert Josef,

Bias in Artificial Intelligence: Exploring its Role in Institutional Discrimination and Strategies for Mitigation /Hubert Josef Oberhauser. - 1 electronic resource (56 pages)

Source: Masters Abstracts International, Volume: 86-10.

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.

English

ISBN: 9798311945899Subjects--Topical Terms:

619355
Software.

Bias in Artificial Intelligence: Exploring its Role in Institutional Discrimination and Strategies for Mitigation /
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520 $a 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.
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