Social science researchers are increasingly leveraging citizen-science mobile applications and crowdsourced public datasets to map urban behavioral trends and community dynamics in real time. While these public platforms offer unprecedented sample sizes, they frequently bypass traditional institutional vetting and expose vulnerable populations to algorithmic surveillance or unintended data leaks. How do you prepare undergraduate researchers to critically evaluate the ethical vulnerabilities of open-source datasets before using them in fieldwork projects? Please share the data literacy exercises or ethical guardrails that have worked well in your methodology courses.
