Humans and algorithms perceive data differently and it is easy to assume that computers are reacting to what humans focus on, such as the shape of a face or the sophistication of text. However, algorithms frequently rely on elements of the data that humans ignore, such as the background colors, angles of photos, or isolated pixels. The result is that humans need to exercise caution when interpreting the results of AI algorithms.
- [Instructor] Back in the early 1900s,…a man named William von Osten had a horse…that he called Clever Hans…and Hans apparently was very smart.…Hans could read, could answer math questions,…history questions, and von Osten actually…made a lot of money taking him around.…This was part of a cottage industry…of smart animals who apparently could do…all sorts of things.…Around the same time, a German biologist…and psychologist named Oskar Pfungst…decided to conduct a rigorous study on Hans…'cause he thought it was probably unlikely…that a horse actually knew how to do math…and calendars and things like that.…
Not surprisingly, he found that Hans was not in fact…an educated horse and he wrote it up…in his 1911 book, Clever Hans the Horse of Mr. Von Osten…as a contribution to experiential animal…and human psychology.…What he found is that instead of actually understanding…all these academic topics,…Clever Hans simply knew that when a person…looks in you in the eyes and they start saying something…and then they look down at your feet…
- 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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