Can AI Generate Diversity?

The video discusses the Draw-a-Scientist Test, highlighting gender disparity in children's perceptions of scientists. It explores challenges in data representation and introduces synthetic data as a solution. The video covers applications of synthetic data, such as data privacy and fraud detection, while addressing ethical concerns like exploitation and bias. It concludes with a discussion on bias in machine learning and the importance of diversity.

What was a significant finding of the Draw-a-Scientist Test conducted by David Chambers?

• The majority of students refused to participate.

• All drawings were of scientists in lab coats.

• Only a small percentage of drawings depicted female scientists.

• Most students drew scientists as animals.

What is a major challenge in collecting data from marginalized groups?

• These groups do not use social media.

• There is a risk of exploiting these groups.

• It is easy and inexpensive to collect such data.

• Data from these groups is always inaccurate.

Why is synthetic data considered beneficial?

• It is only used for entertainment purposes.

• It eliminates the need for any data analysis.

• It can be generated quickly and cost-effectively.

• It is always more accurate than real data.

How can synthetic data help in protecting user privacy?

• By sharing real user data openly.

• By generating data with a similar distribution to private data.

• By eliminating the need for data encryption.

• By storing data in physical locations only.

What is a potential ethical issue with using AI-generated images?

• They are too expensive to produce.

• They can exploit individuals whose images are used without consent.

• They are always of low quality.

• They are easily recognizable as fake.

What is a limitation of using machine learning for diversity?

• It always results in perfect diversity.

• It can amplify existing biases in data.

• It requires no human intervention.

• It is only applicable to scientific research.

Why might relying on synthetic data create a false sense of diversity?

• Because it is based on a limited distribution of existing data.

• Because it is too diverse to be useful.

• Because it always includes every possible demographic.

• Because it is only used in entertainment.