rai.tar.gz

fairness

jupyter

respai

This course offers a comprehensive exploration of the ethical considerations and best practices surrounding the development and deployment of artificial intelligence (AI) systems, all within the context of Python. 

In this course, you will focus on the key pillars of Responsible AI, starting with the notion of fairness. You’ll gain insight into the presence of biases across the AI life cycle and learn strategies for identifying and mitigating bias in AI solutions. The course emphasizes the significance of fairness in various domains, including healthcare, and equips learners with the tools to navigate fairness considerations effectively. Furthermore, exploring Explainable AI sheds light on methods for interpreting and understanding AI model decisions, which are crucial for ensuring transparency and accountability in AI systems. 

After taking this course, you’ll be equipped with the knowledge and tools to promote ethical AI development and uphold principles of responsibility and accountability.

Responsible AI: Principles and Practices

The AI system predicts job performance accurately for both male and female candidates.

The AI system consistently rejects candidates of Asian origin.

The AI system score does not vary based on the race or color of the candidate, keeping other factors constant.

Keeping other factors constant, the AI system assigns similar scores to candidates, regardless of their age.

In an AI system used for hiring, which scenario would be considered a fairness concern?

In an AI system used for hiring, which scenario would be considered a fairness concern?

Training the AI system using historical loan data without considering the demographic information of applicants.

Incorporating demographic information of applicants as a direct input to the AI system for decision-making.

Regularly monitoring and evaluating the AI system’s outcomes for any disparities based on protected attributes.

Ignoring fairness considerations and focusing solely on maximizing loan approval rates.

A company is developing an AI system for loan approvals. Which approach would promote fairness in this context?

A company is developing an AI system for loan approvals. Which approach would promote fairness in this context?

Which statistical metric is used to assess whether an AI system provides equal opportunity for different groups in terms of positive outcomes?

Which statistical metric is used to assess whether an AI system provides equal opportunity for different groups in terms of positive outcomes?

Equalizing the true positive rates across different groups.

Balancing the false positive rates between different groups.

Ensuring equal representation of demographic groups in the outcomes.

Maximizing the overall accuracy of the AI system.

What does the demographic parity metric aim to achieve in fairness evaluation?

What does the demographic parity metric aim to achieve in fairness evaluation?

Which option can contribute to bias in AI solutions?

Which option can contribute to bias in AI solutions?

Test your understanding of fairness concepts in Responsible AI.

Introduction to Responsible AI

Fairness of AI Solutions

Explainable AI

Data Privacy, Safety, and Security for Responsible AI

Innovations in Responsible AI: Charting New Frontiers

Final Project

Conclusion

Quiz: Fairness of AI Solutions