Navigating the Ethical and Privacy Concerns of Big Data and Machine Learning in Decision Making

Abstract

In recent years, the fields of big data and machine learning have gained significant attention for their potential to revolutionize decision-making processes. The vast amounts of data generated by various sources can provide valuable insights to inform decisions across a range of domains, from business and finance to healthcare and social policy. Machine learning algorithms enable computers to learn from data and improve their performance over time, thereby enhancing their ability to make predictions and identify patterns. This article provides a comprehensive overview of how big data and machine learning can improve decision-making processes between 2017–2022. It covers key concepts and techniques involved in these tools, including data collection, data preprocessing, feature selection, model training, and evaluation. The article also discusses the potential benefits and limitations of these tools and explores the ethical and privacy concerns associated with their use. In particular, it highlights the need for transparency and fairness in decision-making algorithms and the importance of protecting individuals' privacy rights. The review concludes by highlighting future research opportunities and challenges in this rapidly evolving field, including the need for more robust and interpretable models, as well as the integration of human decision making with machine learning algorithms. Ultimately, this review aims to provide insights for researchers and practitioners seeking to leverage big data and machine learning to improve decision-making processes in various domains.