Ethics, privacy, and foundations of data governance
Description: Lecture 2 follows the first access to GEIH by asking what responsible use of that data requires. We treat harm, consent, and fairness not as a final checklist but as requirements that shape every technical choice in Big Data projects. The readings and a guided ethics audit on survey and map data prepare the accountable-practice frame for storage, processing, and analytics ahead.Department: Departamento de Matemáticas y Estadística - Facultad de Ciencias Exactas y Naturales
Institution: Universidad de Nariño
Date: June 06, 2026
Hours: 5
From: 07:00 am
To: 01:00 pm
Week 1 links
Resources
- OpenStreetMap — Nariño
- Overpass API
- Overpass by Example — building queries (es)
- Overpass QL syntax (reference) · Overpass Turbo (try queries in the browser)
- GEIH 2024 — data dictionary (DANE)
- UK Data and AI Ethics Framework
References
- Zuboff, S. (2019). The age of surveillance capitalism (Chapter 1). PublicAffairs.
- Mittelstadt, B. D., et al. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2).
- Prabhakaran, V., et al. (2022). A human rights-based approach to responsible AI. arXiv:2210.02667.
- Afroogh, S., et al. (2024). Embedded ethics for responsible AI systems in disaster management. AI and Ethics, 4, 1117–1141.
- UK Government. (2025). Data and AI Ethics Framework.
- Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148–152.
- Nissenbaum, H. (2004). Privacy as contextual integrity. Washington Law Review, 79(1), 119–158.
- boyd, d., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662–679.