Guiding Principle - Fairness

The video discusses the principle of fairness in responsible AI, emphasizing the need to treat all individuals equally regardless of their background. It provides examples of fairness in applications like bank loans, medical treatment, and employment. A case study of Amazon's AI recruiting tool highlights gender bias issues. Guidelines for training AI models to ensure fairness are provided, along with sample exam questions on the topic.

What is the primary focus of fairness in responsible AI?

• Treating all individuals equally regardless of background

• Ensuring all AI systems are fast

• Maximizing profits for companies

• Reducing the cost of AI development

In the context of AI, why is it important to use diverse datasets?

• To reduce the cost of data storage

• To make the AI model more complex

• To ensure the AI model is not biased towards a particular group

• To increase the speed of data processing

What was the issue with Amazon's AI recruiting tool?

• It only accepted applications from certain universities

• It was biased against women

• It favored candidates from urban areas

• It was too slow in processing applications

What principle is violated when AI systems monitor people based on ethnicity?

• Transparency

• Security

• Fairness

• Efficiency

Cultural denigration in AI systems is a violation of which principle?

• Privacy

• Fairness

• Accuracy

• Innovation