AI Bias: Causes, Impacts, and Ways to Address It - Rishu Chaudhary; Rajnandani Rathore; Akanksha Sharma; Sanjeev Patwa (International Journal of Algorithms Design and Analysis Review, 2025)
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: AI accountability; AI regulation; Algorithmic bias; Artificial Intelligence (AI); Discrimination; Machine Learning; Transparency; ai ethics; algorithmic fairness; bias mitigation; data preprocessing; ethical guidelines; fairness in AI; responsible AI; social impacts of AI