Inside AI: What Every Professional Needs to Know to Stay Relevant, Protected, and Ahead
summary
LinkedIn rolls out an opt-out policy that feeds user posts and metadata into training data while email platforms and social apps embed AI features like suggested replies and summaries; an internal Amazon hiring model trained on ten years of resumes reflects past bias — at the time 63% of employees and 75% of managers are male and the model penalizes “women” — a DC teacher loses her job to the Impact Assessment algorithm and a jury rules Instagram/YouTube are addictive. Use cases: ChatGPT and Claude give opposing, confidence‑biased advice.
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chapters (11)
- 0:00AI and data collection
- 1:59You power AI systems
- 7:36Bad data bad AI
- 8:19AI can't measure
- 13:15Bias in training data
- 16:49Bad data scales fast
- 20:36AI is a literacy problem
- 21:18Three habits for AI use
- 23:48Fact-check AI outputs
- 26:39Improve AI usage
- 29:53AI using you
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