Creating Global Economic Opportunity with Responsible Data Science
summary
LinkedIn processes over a terabyte of events daily with roughly 500,000 jobs running to power the economic graph and pursues making Differential Privacy the default for external data sharing, releasing a top-K DP algorithm and deploying a differential privacy meter that includes privacy budget management. Analysis finds only about 20% of AI/data science professionals are female and shows structural network diversity predicts job mobility; fairness work uses the Equal Opportunity framework and measures skewness at K to detect concentrated benefits.
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chapters (20)
- 0:00Introduction
- 1:07The Economic Graph
- 2:13Skills Trends
- 2:47Gender Gap
- 3:55Pool Button
- 4:25Data Infrastructure
- 5:02Data Responsibilities
- 5:29Privacy
- 5:42Identity
- 6:11Differential Privacy
- 7:39Data Applications
- 8:26Global Differential Privacy
- 9:07Challenges of Differential Privacy
- 9:53Achieving Differential Privacy
- 11:40Algorithm Bias
- 13:17Equal Outcome
- 14:19Fairness Using Data
- 16:03Social Capital
- 16:35Measurement
- 17:32Experimentation
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