Course Description
These lessons on Bias, Inclusivity, and Fairness in Artificial Intelligence will explore why bias is one of the most discussed concepts involving AI ethics and why preventing bias should be a part of the governance of every AI system. Additionally, these lessons will review the concepts of fairness, representativeness, and the principle of inclusive impact.
AI systems scale harmful unfairness to a massive degree. As a result, preventing bias should be a part of the whole model building pipeline from design to data collection to model training and testing. Representative data is important to overcoming bias in machine learning model building. For data to be representative, it must accurately represent the cases the AI is concerned with. You will learn the ways that representative data can be used to mitigate or eliminate bias in machine learning-based AI systems.
At its essence, fairness means that data subjects should be treated impartially and equitably, regardless of characteristics such as race, gender, age, region, nationality, country of origin, political beliefs, religion, etc. Through these lessons, you’ll also explore the concept of fairness and its relevance to bias, the specific definitions of which have been explored at length in the academic literature.
Finally, you’ll learn about the principle of inclusive impact and how it can be used to empower people and share benefits amongst everyone.
What You'll Learn
- Describe the difference between bias and fairness in AI systems
- Recognize the risks posed by biased AI systems
- Explain why representative data is important to creating fair AI
- Apply representative data to mitigate or eliminate bias in machine learning-based AI systems
- Understand the principle of inclusive impact and its ability to empower those interacting with AI
- Explore fairness, representativeness, and the principle of inclusive impact in AI ethics
Key Takeaways
- Bias is one of the most discussed concepts in AI ethics, and preventing it should be part of the governance of every AI system.
- Because AI systems scale harmful unfairness to a massive degree, preventing bias should span the whole model-building pipeline from design to data collection to model training and testing.
- Representative data accurately represents the cases the AI is concerned with and is important to overcoming bias in machine learning model building.
- Fairness means data subjects should be treated impartially and equitably, regardless of characteristics such as race, gender, age, region, nationality, country of origin, political beliefs, or religion.
- The principle of inclusive impact can be used to empower people and share benefits amongst everyone.
Frequently Asked Questions
What topics does this course cover?
It covers bias, inclusivity, and fairness in artificial intelligence, including detecting and preventing bias, representative data, fairness in AI, and inclusive impact.
What skills will I gain from this course?
You will build skills in Artificial Intelligence Systems, Artificial Intelligence, Biasing, Computational Intelligence, Data Ethics, and Diversity Equity and Inclusion Policies.
What does fairness mean in the context of AI?
Fairness means that data subjects should be treated impartially and equitably, regardless of characteristics such as race, gender, age, region, nationality, country of origin, political beliefs, or religion.
Why is representative data important?
For data to be representative it must accurately represent the cases the AI is concerned with, and representative data can be used to mitigate or eliminate bias in machine learning-based AI systems.
What is the principle of inclusive impact?
Inclusive impact is a principle that can be used to empower people who interact with AI and to share benefits amongst everyone.
