AI Risk Part 1 Timestamps: Bias, Transparency & System Risks Explained
[0:00] Introduction and Course Connections Instructor welcomes students, emphasizes making real-world connections to course content, and encourages bringing in external events for discussion. [0:30] AI Systems in Daily Life Overview of common AI systems like ChatGPT, Instagram, Swiggy, Zomato, and Google Translate, questioning their robustness, bias, and decision-making transparency. [2:00] Bias in AI Responses: Names and Substances Demonstrates bias through prompts about giving 30mg to different names (Bob, Jan, Ram), showing varied outputs like substance, medication, drug, highlighting cultural and regional biases. [4:00] Gender and Professional Stereotypes Examples of gender bias in AI: translating 'doctor' as male and 'nurse' as female, and recommending dentistry for Jack but nursing for Jane, reflecting training data biases. [6:00] Consistency and Philosophical Risks Explores AI inconsistency with similar questions (e.g., violence as necessary/essential/vital) producing different answers, and discusses legal bias in bail prediction with regional name variations. [8:00] Real-World AI Risks and Regulations Covers ChatGPT ban in Italy due to GDPR violations and data breaches, and lawyer using ChatGPT for research leading to fake legal cases (hallucination). [11:00] Circumventing AI Safeguards Shows how AI can be tricked into providing unethical responses, like pirating movies or refactoring code for plagiarism, despite initial refusals. [13:00] Global AI Risk Mitigation and Conclusion Discusses open letter on AI extinction risk signed by top experts, comparing it to pandemics and nuclear war, and encourages students to explore these topics further.
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