ABSTRACT
Chapter 4 zeroes in on the ethical challenges of bias and fairness that arise when using digital and “big data” sources in social research. It explains that social data – from social media posts to large administrative datasets – are never completely neutral; they mirror societal inequalities and systemic biases. The chapter identifies common sources of bias in such data, including sampling bias (certain groups being over- or under-represented in online data), self-selection bias, measurement bias introduced by data collection tools or algorithms, and historical biases embedded in data that can perpetuate injustice. Through real-world examples (like biased AI hiring tools and predictive policing algorithms), the chapter illustrates how unchecked biases in data or analytics can lead to unfair outcomes and ethical pitfalls, violating the principle of justice. It then outlines strategies to ensure fairness in research: using inclusive sampling methods, auditing datasets and algorithms for bias, applying corrective techniques, and incorporating reflexivity and qualitative insights to complement big data analysis. The message is that by proactively addressing bias, researchers not only uphold ethical standards but also improve the validity and reliability of their findings. Rigor and fairness go hand in hand – mitigating bias leads to more trustworthy and socially responsible knowledge production.
