PROMOTING ETHICAL AI, STRATEGIES FOR MITIGATING BIAS IN MACHINE LEARNING MODELS

Abstract

As artificial intelligence (AI) becomes an increasingly integral part of our daily lives, the need for ethical AI practices, particularly in addressing bias, has never been more crucial. This article provides a closer examination of the complex issue of bias in AI, examining how it arises from data, algorithms, and human decisions. It underscores why fairness, transparency, and accountability are essential to ensure that AI systems deliver fair outcomes for everyone. The article also discusses practical strategies for detecting and reducing bias, such as sourcing diverse data, applying fairness constraints during algorithm design, and using fairness metrics to evaluate models. Beyond technical solutions, it highlights the importance of involving stakeholders and complying with regulations to guide the ethical development of AI. By tackling these challenges, the article aims to support the creation of AI technologies that are not only innovative but also fair and responsible benefiting society as a whole.

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