Mitigating Bias in AI Research and Development

Abstract

Bias in artificial intelligence (AI) research and development remains a significant challenge, influencing decision-making processes and impacting fairness, accountability, and transparency. This research paper explores the origins of bias in AI systems, examining historical, technical, and ethical dimensions. It further evaluates contemporary mitigation strategies, including algorithmic fairness, data curation techniques, and interdisciplinary approaches. A controlled experiment is conducted to analyze the effectiveness of bias mitigation techniques, comparing traditional methods with emerging solutions. The results indicate that while no single method eliminates bias entirely, a combination of approaches substantially reduces discriminatory patterns in AI models. The findings emphasize the necessity of an inclusive framework that incorporates diverse datasets, fairness-aware algorithms, and ethical AI governance. The paper concludes with recommendations for improving bias mitigation practices in AI research and development to ensure more equitable and reliable AI applications.