Be more responsible around AI - Less Bias, More Ethics
summary
Uploads a phone photo to facecheck.id reverse-search and immediately finds an Instagram profile, demonstrating how easy nonconsensual identification is. Shows Google Translate flipping genders when translating through Armenian, and cites an example where an Amazon hiring algorithm favoured white men because training data reflected existing hires. Mentions deepfakes, a 100% AI influencer with hundreds of thousands of followers, and a DALL·E-style generation failing to put a man in a pink suit; urges documented ownership, external audits, explainability and diverse auditors.
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chapters (17)
- 0:00Introduction
- 1:50AI's rise and dual nature
- 3:19Facial recognition risks demo
- 5:08Bias in translation tools
- 6:53Bias in navigation systems
- 7:15Human biases in AI
- 8:18Diverse perspectives in AI
- 9:22AI transparency and accountability
- 11:08Facial recognition ethics
- 11:55AI bias in hiring
- 12:38Fake accounts from AI
- 13:40AI ownership and accountability
- 14:45Legal and ethical AI frameworks
- 15:26Evaluating AI for organizations
- 16:08AI limitations and biases
- 18:59AI unreliability in contexts
- 20:24AI adoption and ethics
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