ChatGPT and DALL-E: Sell Your Creative Thinking with AI - Examples of Biased AI

The video discusses the prevalence of bias in AI systems, using Amazon's AI hiring tool and facial recognition technology as examples. Amazon's tool showed gender bias due to skewed training data, while facial recognition systems often perform poorly on diverse demographics. The video emphasizes the importance of recognizing and addressing these biases to prevent negative impacts.

What was a significant flaw in Amazon's AI hiring system?

• It only considered applicants from certain regions.

• It was too slow in processing resumes.

• It favored candidates with higher education.

• It showed bias against female candidates.

What action did Amazon take after identifying the bias in their AI hiring tool?

• They retrained the AI with new data.

• They continued using the tool with modifications.

• They decided to discontinue the tool.

• They outsourced the hiring process.

Why do facial recognition programs often perform poorly on certain demographics?

• They are only tested on a small number of people.

• They require high-quality cameras to function correctly.

• They are trained on data that is not diverse enough.

• They are designed to work only in specific lighting conditions.

Which demographic did facial recognition technology perform best on, according to the report?

• People with darker skin tones

• Middle-aged white men

• Women of all ages

• Elderly individuals

What is the responsibility of users regarding AI systems, as mentioned in the final section?

• To ignore the biases and focus on benefits

• To learn about and address the biases

• To rely solely on developers to fix biases

• To use AI systems only in specific fields