When training data is biased in its selection (for example, only faces of white Americans are included) or classification (for example, photos are categorized only by people in one social or economic group), then the resulting predictions and categorizations will also be biased. Again, this is generally seen as a technical problem that, with awareness, can be overcome by assembling more diverse data sets.
- [Instructor] In a 2017 episode of the sitcom…Silicon Valley, our heroes create a smartphone app…called SeeFood that can visually identify food.…When they test it on a hot dog, it correctly identifies it…as a hot dog and everyone is thrilled.…However, when they test it on anything else,…all it's able to say is that it's not a hot dog.…So instead of creating a universal food identifier,…they made a binary hot dog, not hot dog discriminator.…The developers in the show argue that the algorithm…is fundamentally capable of doing more,…but they've only trained it on hot dogs.…
Now of course, this is a comedy…and it's intended as a joke, but it turns out…it's not really that far from reality sometimes.…There are several examples of AI behaving badly.…So for instance, Microsoft created a Twitter bot…called Tay that only took 12 hours to become…a misogynistic, anti-Semitic, conspiracy theorist…hate-spewing machine and it had to be…turned off permanently.…A program called COMPAS that stands for…Correctional Offender Management Profiling…
- 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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