AI Bias: Causes, Impacts, and Ways to Address It

Year : 2025 | Volume : 03 | Issue : 01 | Page : 55 62
    By

    Rishu Chaudhary,

  • Rajnandani Rathore,

  • Akanksha Sharma,

  • Sanjeev Patwa,

  1. Student, School of Engineering and Technology, Narodara Rural, Rajasthan, India
  2. Student, School of Engineering and Technology, Narodara Rural, Rajasthan, India
  3. Student, School of Engineering and Technology, Narodara Rural, Rajasthan, India
  4. Associate Professor, School of Engineering and Technology, Narodara Rural, Rajasthan, India

Abstract

As artificial intelligence (AI) continues to permeate various aspects of society, from healthcare and criminal justice to finance and hiring, concerns over its ethical implications have gained increasing attention. A significant ethical concern is the existence of bias in AI systems. Such biases, often rooted in the prejudices present in training data, can lead to unfair and discriminatory consequences, disproportionately affecting marginalized groups. This paper examines the ethical challenges related to AI, concentrating on the origins and kinds of biases present in machine learning models. It examines the social, economic, and legal implications of biased AI, and discusses potential mitigation strategies, including data preprocessing, algorithmic fairness techniques, and transparent AI practices. The paper also examines regulatory frameworks and ethical standards designed to promote responsible AI development and implementation. Ultimately, the goal is to highlight the critical importance of ethical considerations in AI design, and propose methods to mitigate bias, ensuring that AI technologies contribute to a fairer, more equitable society.

Keywords: Artificial intelligence (AI), AI ethics, bias mitigation, algorithmic fairness, machine learning, discrimination, fairness in AI, ethical guidelines, data preprocessing, transparency, AI accountability, social impacts of AI, algorithmic bias, responsible AI, AI regulation

[This article belongs to International Journal of Algorithms Design and Analysis Review ]

How to cite this article:
Rishu Chaudhary, Rajnandani Rathore, Akanksha Sharma, Sanjeev Patwa. AI Bias: Causes, Impacts, and Ways to Address It. International Journal of Algorithms Design and Analysis Review. 2025; 03(01):55-62.
How to cite this URL:
Rishu Chaudhary, Rajnandani Rathore, Akanksha Sharma, Sanjeev Patwa. AI Bias: Causes, Impacts, and Ways to Address It. International Journal of Algorithms Design and Analysis Review. 2025; 03(01):55-62. Available from: https://journals.stmjournals.com/ijadar/article=2025/view=201579


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Regular Issue Subscription Original Research
Volume 03
Issue 01
Received 29/01/2025
Accepted 08/02/2025
Published 22/02/2025
Publication Time 24 Days
204

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