Senior Academic Session I
summary
A taxonomy distinguishes individual vs societal harms and separates harms tied to raw data processing from harms tied to outputs and inferences; the GDPR proves useful for conventional data-processing risks but limited or misaligned for outcome-driven and societal effects. Practical proposals pair multi-layered explanations tied to DPIAs so controllers map, assess and redesign high-risk automated systems. A regulatory comparison (GDPR, CCPA, APPI) finds no fundamental conflicts, so an automotive ride‑hailing system can be engineered to comply by adopting the strictest applicable safeguards, though technical privacy and cross‑border transfer challenges remain.
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chapters (10)
- 0:00Start
- 10:19Research Question
- 30:47Accountability Principle
- 34:43Transparency Fallacy
- 35:37Multi-Layered Explanation
- 36:58General Information Layer
- 38:19Data Protection Impact Assessment
- 39:47Assessment of Necessity and Proportionality
- 51:29Ally Alma Manning
- 53:15Automotive Industry
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