Impact of Data Science
Context: FIT1043_MOC · where Data Science touches society · benefits weighed against privacy/ethics · three lenses — cloud, social good, futurology
Quick Revision
- 🎯 Objective: appraise data science’s societal impact ➔ life in the cloud, social good, and futurology.
- ⚡ Key Constraint: every benefit has an ethics/privacy counter-cost — datafication empowers personal agents and enables surveillance/breaches.
📝 Core
1. Your Life in the Cloud (datafication)
- Datafication ➔ personal life increasingly stored in the cloud: social (Facebook), career (LinkedIn), search (Google), health (Fitbit), music (Spotify).
- Upside ➔ personal agents, computerised health support.
- Downside ➔ security/privacy breaches; corporate→government leakage; no right to access/delete your own data; “pre-crime”; profiling despite having changed; social scoring.
2. Social Good
- DS for Social Good ➔ movement training data scientists to support community/charity (e.g. fighting accidents and disease; Teradata University Network, SimplyGiving).
3. Futurology
- Healthcare ➔ instrumenting stomach/bloodstream; a shared online “health dashboard” for your GP; HMOs tying funding to care performance; longitudinal studies (same subjects measured repeatedly over time) become feasible.
- Automobiles ➔ self-driving cars raise systemic questions: replacing traffic-fine revenue, drink-driving liability, taxi and auto-insurance industries, insuring human drivers.
⚠️ Common Mistakes
- 💡 Benefit ⇄ cost is inseparable ➔ the same data enabling personalised health support enables breaches, surveillance, and profiling; impact analysis must weigh both.
- 💡 Impact is systemic, not just technical ➔ self-driving cars disrupt revenue, insurance, and whole industries — not only driving.
🧠 Active Recall
Give one benefit and one risk of "datafication", and define a longitudinal study.
Answer
- Short answer: Benefit — personal agents/computerised health support; risk — security/privacy breaches, profiling, or loss of control over your own data. A longitudinal study gathers data on the same subjects repeatedly over time.
- Why: Two-sided datafication ➔ storing life in the cloud powers both helpful services and surveillance/pre-crime scenarios.