Ethical Issues in Social Science Research Employing Big Data

Area of Science:

• Social Sciences
• Data Science
• Research Ethics

Background:

• The intersection of big data ethics, social science research (SSR), and research ethics presents a significant knowledge gap.
• Big data in SSR requires special ethical consideration due to its interpretive nature, complex risk management, and limited regulatory oversight.

Purpose of the Study:

• To analyze the ethical considerations of big data in social science research.
• To identify and address key ethical issues arising from big data methodologies in SSR.
• To propose a framework for developing future ethical guidelines in this domain.

Main Methods:

• Analysis of ethical issues in big data social science research.
• Application of David Resnik's research ethics framework.
• Examination of principles including honesty, carefulness, openness, efficiency, respect for subjects, and social responsibility.

Main Results:

• Identified three clusters of ethical issues: methodological biases and personal prejudices, risks from data availability and reuse, and potential individual and social harms.
• Highlighted the interpretative nature of both SSR and big data as a key ethical consideration.
• Emphasized the need for enhanced regulatory oversight and ethical recommendations.

Conclusions:

• David Resnik's research ethics framework provides a valuable tool for analyzing big data SSR ethics.
• There is a critical need for developing robust ethical guidelines to protect individuals and societies in big data SSR.
• Future guidelines should address methodological biases, data reuse risks, and potential harms to ensure responsible research practices.