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Ethical Considerations in Machine Learning: Bias, Fairness, and Accountability
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: https://doi.org/10.64388/IREV9I11-1717813
Abstract
Machine Learning (ML) has become a transformative technology across domains such as healthcare, finance, governance, and social media. However, its widespread adoption raises significant ethical concerns related to bias, fairness, and accountability. This paper examines how biases emerge from datasets and algorithmic design, often leading to discriminatory outcomes. It explores fairness frameworks and highlights the challenges of achieving equitable decision-making. The study also addresses accountability issues in opaque “black-box” systems and emphasizes the importance of transparency and governance. A case study on automated hiring systems illustrates real-world ethical challenges. The paper concludes by proposing practical strategies for responsible and ethical ML development.
How to cite this paper
@article{1717813,
author = {Radhika Rajput, Abrashmeena Shaikh},
title = {Ethical Considerations in Machine Learning: Bias, Fairness, and Accountability},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {1847-1849},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717813.pdf},
abstract = {Machine Learning (ML) has become a transformative technology across domains such as healthcare, finance, governance, and social media. However, its widespread adoption raises significant ethical concerns related to bias, fairness, and accountability. This paper examines how biases emerge from datasets and algorithmic design, often leading to discriminatory outcomes. It explores fairness frameworks and highlights the challenges of achieving equitable decision-making. The study also addresses accountability issues in opaque “black-box” systems and emphasizes the importance of transparency and governance. A case study on automated hiring systems illustrates real-world ethical challenges. The paper concludes by proposing practical strategies for responsible and ethical ML development.},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717813}
}