This article examines the societal implications of big data and distributed computing technologies, with particular focus on algorithmic bias mitigation and privacy protection. As these technologies transform decision-making across healthcare, finance, and criminal justice, they introduce complex ethical considerations that require thoughtful responses. The paper explores how biases in training data perpetuate social inequities, creating disparate impacts for vulnerable populations, while analyzing the mathematical constraints that make satisfying multiple fairness criteria simultaneously impossible. It also investigates how distributed computing architectures enhance privacy through differential privacy, federated learning, and blockchain-based consent management, enabling organizations to derive insights while maintaining privacy guarantees and regulatory compliance. The research reveals that addressing bias requires comprehensive approaches spanning the entire development lifecycle, from data curation to continuous monitoring. Similarly, privacy protection demands more than technical solutions alone, requiring governance frameworks that navigate tensions between competing privacy principles. Through examination of implementation challenges and governance models, the article provides a balanced assessment of responsible deployment strategies that maximize benefits while minimizing harms, emphasizing multi-stakeholder governance, transparent documentation, and contextual regulation as essential components of ethical technological advancement.
Keywords: algorithmic bias, differential privacy, ethical governance, federated learning, privacy-preserving computation
