How To Make Algorithms Fairer | Algorithmic Bias and Fairness

The video explores how AI can unintentionally advance systemic racism by amplifying biases. It discusses the challenges in defining fairness in AI and the importance of formulating unbiased problem statements. The video also covers data collection issues, such as class imbalance and bias amplification, and suggests methods to mitigate these biases. External auditing and ethical considerations in AI development are highlighted as crucial steps in creating fair AI systems.

How can AI unintentionally contribute to systemic racism?

• By ignoring biases altogether

• By creating new biases

• By amplifying existing biases

• By eliminating biases in data

What is a major challenge in defining fairness in AI?

• Over-reliance on technology

• No general benchmark for fairness

• Historical biases in algorithms

• Lack of data

Why is it important to define the problem correctly in AI development?

• To increase the speed of computation

• To avoid using complex algorithms

• To ensure the model answers the intended question

• To reduce the cost of development

What is a common issue with using existing data sets in AI?

• They are always too large

• They often contain class imbalances

• They are too expensive to use

• They are always outdated

What is bias amplification in AI models?

• The creation of new biases in data

• The reduction of bias in data

• The elimination of bias through algorithms

• The increase of bias during model training

What is one method to reduce bias amplification in AI models?

• Increasing the size of the data set

• Ignoring the bias altogether

• Removing parts of images associated with bias

• Using outdated algorithms

What is the role of external auditing in AI bias mitigation?

• To reduce the cost of AI development

• To ensure algorithms are as fair as possible

• To develop new algorithms

• To increase the speed of AI systems

Why might some algorithms not be developed at all?

• They have no significant positive impact

• They are too expensive to create

• They are not technologically feasible

• They are too complex to understand

What is a potential downside of balancing data sets to reduce bias?

• It makes the data set too large

• It eliminates all biases

• It can still result in bias amplification

• It always leads to overfitting

What is a controversial idea in the machine learning community regarding bias?

• To stop developing certain algorithms

• To increase the complexity of models

• To use only historical data

• To ignore ethical considerations