Ethical Implications of Data Analytics in Everyday Applications

Abstract

This thesis explores the ethical challenges posed by the widespread application of data analytics in contemporary society. While data analytics has enhanced decision-making across healthcare, finance, and governance sectors, its use raises serious concerns related to privacy, algorithmic bias, discrimination, and data misuse.

This thesis identifies key risks associated with data-driven technologies through a critical literature review and real-world case studies—such as Cambridge Analytica, Amazon's biased hiring algorithm, and major data breaches. It further evaluates regulatory frameworks like the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and California Consumer Privacy Act (CCPA) and introduces tools and strategies for mitigating bias and promoting transparency.

Emphasis is placed on the role of organizations in fostering ethical data practices through explainable AI, algorithmic audits, and inclusive governance. The research concludes with practical recommendations for ethical data usage and outlines future directions for promoting fairness, accountability, and trust in data analytics. By bridging technical practice and moral responsibility, this thesis aims to support the development of ethically sound and socially responsible data systems.