Navigating AI Ethics: Addressing Bias in Machine Learning Models

Abstract

As artificial intelligence (AI) becomes increasingly integrated into various facets of society, the ethical implications of AI technologies, particularly concerning bias in machine learning models, have come under scrutiny. Biases present in training data, algorithms, and decision making processes can lead to discriminatory outcomes, exacerbating societal inequalities. This paper explores the ethical considerations surrounding bias in machine learning models, delving into its causes, manifestations, impacts, and mitigation strategies. By navigating the complex terrain of AI ethics, this paper aims to foster awareness and discourse on the imperative of addressing bias to ensure the responsible and equitable deployment of AI technologies.