Classification errors in AI can result from difference in base rates, lack of diversity in training data, and lack of diversity of raters, but these are all challenges that can be overcome once their existence is acknowledged.
- [Instructor] Giving the existence of classification errors…in artificial intelligence, a big question is,…what's causing them and what can you do about it.…Well, one important thing to think about…actually comes from mathematics, and that's Bayes' Theorem.…And what Bayes' Theorem does is it tells you the probability…of an accurate conclusion given the data…is affected by several factors,…and these include the probably of true positives.…So people who should be put into a particular category,…what percentage of them are.…Also, the probability of false positives,…people who should not be put into that category,…as some of them will be put into the category anyhow,…and then there's the base rate of the outcome.…
How common is the category that you're concerned about?…Now, let's give an example of how this all works…by looking at medical diagnoses,…and the identifying of rare diseases.…Some diseases are really rare,…and you can diagnose them based on blood tests, MRIs,…EKGs, or whatever.…And assume you're talking about diseases…
- Bias in AI
- Navigating the social challenges of AI
- Moral reasoning and relational ethics
- General Data Protection Regulation (GDPR) and AI
- Discrimination in data
- Liability and AI
- AI in life-and-death situations
- Confronting the challenges of AI as a developer, executive, and consumer
Skill Level Intermediate
1. The Context for AI
2. Technical Challenges of AI
3. Social Challenges of AI
4. Legal Challenges of AI
5. Safety Challenges of AI
AI in the military4m 36s
6. Confronting the Challenges of AI
Next steps2m 52s
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