Examining Trust and Transparency in AI Decision-Making

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This chapter explores the dangers of utilizing non-explainable machine learning models in critical fields like self-driving cars and the justice system. It emphasizes the importance of transparency and trust in AI decision-making, especially in high-stakes scenarios where human lives are affected.

1:04 Jason intros Alexandr
2:19 Alexandr shares his personal startup history
5:17 How & why did Scale start?
8:26 What is the best example of Scale in practice? What problem are they solving?
10:44 Video demo of Scale's platform
15:34 Acquiring the scale.com domain name & insights on the unique spelling of Alexandr
17:31 How does Scale deal with data-sharing between customers?
21:34 LIDAR vs. non-LIDAR... or both?
32:29 When will we have capable self-driving vehicles from Palo Alto to San Francisco? Over/under 2030? How will gov't regulations affect self-driving?
36:03 China vs. US in the race of self-driving
41:22 Explainability in ML
47:26 Does it matter that we sometimes don't know the answer to ML systems?
51:39 Should explainability have to be proven in ML?
55:13 How should inherently biased data-sets (like US justice system) be handled via ML?
1:00:00 Importance of focusing on higher-value work
1:02:41 Are dangers of AI overblown?
1:08:50 Will "General AI" happen in our lifetime?
1:12:26 What's the next major AI trend after self-driving?
1:23:46 Does Alexandr remember a time before the Internet?
1:26:35 Jason plays "good tweet/bad tweet" with Alexandr