Teaching Data Ethics and Privacy: Strategies for Data Science Programs

Abstract

Abstract. The acceleration of digital transformation, Big Data analytics, and Artificial Intelligence (AI) implementation has fundamentally reshaped operational landscapes across sectors, ranging from academic governance to MSME commercialization. However, this technological leap has triggered unprecedented crises in information ethics, privacy breaches, and massive data security vulnerabilities. This article aims to synthesize theoretical foundations, global bibliometric research trends, field-level security challenges, and interdisciplinary pedagogical strategies based on state-of-the-art literature. By integrating the concept of Distributed Moral Responsibility (DMR), large-scale global bibliometric trend analysis, technical risk evaluations (such as national ID card leaks and e-commerce breaches), and top-to-bottom capstone-integrated curricula, we formulate a responsive data governance framework. This paper asserts that enforcing data ethics requires a synergistic alignment between regulatory compliance (such as the Personal Data Protection Act/UU PDP), secure technical architectures, and the reconstruction of transversal higher education curricula.