Research entry
Data Ethics Essays
2021 · Academic Archive
Two ethics essays exploring the ethical implications of large-scale biometric data systems (Aadhaar) and a broader case study on data ethics in modern technology.
Overview
Ethics coursework exploring real-world data ethics challenges. The first essay analysed India’s Aadhaar biometric identity system — its design, data risks, and ethical implications for 1.3 billion citizens. The second was a broader case study on ethical responsibilities of data practitioners.
Essay 1: Aadhaar — Biometric Identity at Scale
[Download PDF](/college/onedrive/DKIT_College/Ethics/Ethics_CA1-Aadhaar Project_ConnorFaulkner.pdf)
The Aadhaar system is the world’s largest biometric database, storing fingerprints and iris scans for over 1.3 billion Indian citizens. This essay examined:
- Data collection scope — what biometric data is collected and how it’s stored
- Privacy risks — centralised storage, breach history, and de-anonymisation risks
- Informed consent — whether citizens meaningfully consent to enrolment
- Mission creep — expansion of Aadhaar use beyond its original welfare purpose
- Discrimination risks — exclusion of vulnerable populations due to biometric failures
Key argument: The scale and irreversibility of biometric data collection demands a higher ethical standard than traditional data systems. Unlike passwords, fingerprints cannot be changed after a breach.
Essay 2: Data Ethics Case Study
A broader case study exploring ethical frameworks applicable to data science practice, including:
- Utilitarianism vs deontological approaches to data decisions
- The GDPR’s right to explanation for automated decisions
- Algorithmic bias and fairness in ML systems
- Ethical responsibilities of the individual data practitioner
Reflection
These essays shaped how I think about the data work I do — particularly around the genomics and population genetics projects, where the data involves real people’s genetic information. Responsible data science isn’t just about accuracy; it’s about understanding who the data represents and what the consequences of analysis can be.