Ethical Considerations in Machine Learning: Bias, Fairness, and Accountability

• Author(s): Radhika Rajput; Abrashmeena Shaikh
• Paper ID: 1717813
• Page: 1847-1849
• Published Date: 15-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

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.

Citations

IRE Journals:
Radhika Rajput, Abrashmeena Shaikh "Ethical Considerations in Machine Learning: Bias, Fairness, and Accountability" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 1847-1849 https://doi.org/10.64388/IREV9I11-1717813

IEEE:
Radhika Rajput, Abrashmeena Shaikh "Ethical Considerations in Machine Learning: Bias, Fairness, and Accountability" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717813