ABSTRACT
This chapter examines how transparency and openness can help address ethical challenges in social data research. It introduces the concept of “digital trespass,” referring to the use of publicly available online data without proper consent, and stresses the importance of informed consent even in digital environments. Privacy is presented as a key concern, requiring researchers to anonymize and aggregate data, as demonstrated in studies using geo-tagged social media data. The chapter discusses the growing use of AI and machine learning in data analysis, highlighting risks related to algorithmic bias and lack of transparency. To support reproducibility and accountability, it recommends openness in research methods, including sharing data, code, and model documentation, as well as conducting bias audits when using algorithmic tools. A global perspective is also emphasized, encouraging researchers to consider cultural differences when applying ethical standards, particularly in consent procedures. The chapter concludes that open science practices, such as data sharing and pre-registration, should be balanced with strong privacy protections. It recommends the continued development of ethical guidelines and the implementation of “ethics by design” in research processes.
