Lead the GenAI revolution by learning AI fairness principles. Future-proof your skills with Python, ensuring fair algorithms for structural and textual data to create unbiased user experiences.

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02 build model

02 eda

02 is fair

02 unawareness

Project: threshold optimization

03 demographic parity

03 equality_of_opportunity.ipynb

03 equalized_odds

03 predictive_parity

04 TO

04 reductions

05 biased

05 debiased

05 weat

05 aug

05 aug-copy

05 weat-copy

In this course, you will learn the basic concepts of AI fairness as part of the broader concept of Responsible AI. As AI’s role in our daily lives increases, ensuring algorithms are fair for everyone is becoming increasingly important. 

You will use Python with various types of models, starting from simple regression up to transformers. You will learn what AI fairness is, how to measure if a model is fair, and, most importantly, how to fix biased systems. You’ll cover both structural (tabular) and textual data.

After completing this course, you will be able to identify areas where fairness is a concern and plan a strategy for ensuring an unbiased user experience. This strategy will start with identifying bias sources by measuring the problem is seriousness and then implementing remedies. This will help you create better models, especially in areas where your actions significantly impact human lives.

Fundamentals of AI Fairness

Yes, because it always reflects negative societal stereotypes.

No, because some biases can be useful for specific linguistic tasks.

Yes, because it affects the accuracy of the model in all contexts.

No, because biases in word vectors do not impact model performance.

Is bias in word vectors always considered harmful or undesirable?

Is bias in word vectors always considered harmful or undesirable?

The tendency of embedding models to represent all genders equally

A specific vector direction that captures the gender aspect in the word vector space

The process of removing gender bias from word embeddings

The direction of evolving gender norms reflected in language models

In the context of word embeddings like GloVe, what does the _gender dimension_ refer to?

In the context of word embeddings like GloVe, what does the gender dimension refer to?

Altering the vector dimensions, for example, from 50 to 300

Identifying and mitigating embedded societal biases

What is a primary challenge in addressing bias in static word embeddings like GloVe?

What is a primary challenge in addressing bias in static word embeddings like GloVe?

Reducing the dimensionality of the vectors

Identifying bias directions in the vector space

Retraining the model with a balanced dataset

Projecting vectors onto a subspace orthogonal to the bias direction

**(Select all that apply.)** Which step is typically involved in a vector debiasing algorithm?

(Select all that apply.) Which step is typically involved in a vector debiasing algorithm?

To measure the similarity between different word embeddings

To evaluate the accuracy of word embeddings in semantic tasks

To assess the presence and extent of biases in word embeddings

To compare the performance of static and contextual word embeddings

What is the primary purpose of the Word Embedding Association Test (WEAT)?

What is the primary purpose of the Word Embedding Association Test (WEAT)?

Which type of bias can WEAT be used to detect in word embeddings?

Which type of bias can WEAT be used to detect in word embeddings?

Generating synthetic examples of underrepresented classes

Removing features that are correlated with sensitive attributes

Which is a common technique used in data augmentation to promote fairness in classifiers?

Which is a common technique used in data augmentation to promote fairness in classifiers?

**(True or False)** Data augmentation always leads to fairer and more unbiased classifiers.

(True or False) Data augmentation always leads to fairer and more unbiased classifiers.

Introduction to AI Fairness

Motivating Example

Measuring Fairness

Project: Fix Bias Using Threshold Optimization

Mitigation Methods

Project: Make a Fair Classifier

Fairness in Natural Language Processing

Project: Explore Fairness in NLP Models

Conclusion

Check Your Knowledge About Fair AI

Quiz: Fairness in NLP