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Ethical AI and Bias Mitigation in Machine Learning Systems

Year: 2025

Type: article

Abstract: Machine Learning (ML) systems are increasingly shaping decisions in healthcare, finance, hiring, and other critical domains. However, biases in data and algorithms can lead to unfair outcomes, exacerbating societal inequalities. This paper explores the sources of bias in AI models, methods for bias mitigation, and frameworks for ethical AI development. We discuss techniques such as fairness-aware learning, adversarial debiasing, and explainability approaches to ensure accountability. Finally, we outline future directions for research in making AI systems more equitable and trustworthy.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17200496

Author: Nikhil

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: green

OpenAlex ID: W7083336145

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