Ethical considerations in data science: Balancing privacy and utility

As data science continues to permeate diverse domains, the ethical interplay between privacy and utility has emerged as a critical concern. This study meticulously investigates this intricate balance by examining established ethical frameworks, scrutinising the ethical implications of federated learning, and proposing a user-centric approach to obtaining informed consent. A total of 243 participants contributed to the study, providing insights from various demographic backgrounds. The investigation into ethical framework adaptation revealed a nuanced landscape of perspectives. While a significant proportion acknowledged the potential of ethical frameworks to address privacy-utility complexities, a diversity of viewpoints underscored the ongoing need for their refinement. Examining federated learning's ethical implications exposed heightened concerns about algorithmic biases and transparency challenges, highlighting the urgency of addressing fairness and accountability in privacy-preserving techniques. Synthesising these findings, the study underscores the evolving nature of ethical considerations in data science and the imperative for continual recalibration. The implications extend beyond academia, offering actionable insights for policymakers, industry practitioners, educators, and stakeholders. The study concludes by recognizing its limitations and advocating for further exploration, emphasising the need for collaborative efforts to create an ethical data landscape that safeguards societal values and individual rights.