Counterfactual Fairness

Summary

This talk delves into the concept of Counterfactual Fairness in machine learning, highlighting issues like racial and gender biases in algorithms. The speaker introduces a causal model approach to address unfairness by considering how sensitive attributes influence decisions. The proposed solution involves a metric and algorithm for learning fair classifiers, demonstrated through an example of law school admission. The talk concludes with a discussion on the practical application of these models and the challenges of ensuring fairness in machine learning.

Takeaways

• 🧠 The talk emphasizes the impressive capabilities of machine learning, such as surpassing human performance in image classification and game playing, but also highlights the need to address significant problems like bias and discrimination.
• 🔍 The speaker introduces the concept of Counterfactual Fairness, which is about creating algorithms that do not discriminate based on sensitive attributes like race or sex.
• 🤖 The talk discusses the limitations of 'Fairness Through Unawareness', where simply removing sensitive attributes from a model does not guarantee fairness due to the influence of these attributes on other features.
• 📈 The 'Equality of Opportunity' approach by Hardt et al. is mentioned, which corrects for unfairness by using sensitive attributes but has limitations as it does not account for biases in the target label itself.
• 🔗 The importance of causal models is stressed to understand how sensitive attributes like race and sex can influence other variables and lead to unfair outcomes.
• 📊 Counterfactuals are introduced as a method to evaluate fairness by imagining what the classifier's prediction would be if a person's sensitive attributes were different, thus allowing for a single change to be observed in its effects.
• 📚 The speaker proposes a learning algorithm that uses causal models to create fair classifiers by only considering features that are not descendants of sensitive attributes.
• 📉 The trade-off between fairness and accuracy is acknowledged, as fair classifiers may have lower predictive accuracy due to the exclusion of biased information.
• 📝 The practical application of the proposed method is demonstrated using a dataset of US law school students, showing the impact of different approaches on fairness and accuracy.
• 🤝 The talk concludes by emphasizing the role of causal models in addressing unfairness in machine learning decisions and the need for further research in this area.
• 🙏 The speaker thanks the co-authors and the audience, inviting questions and discussion on the presented topic.

Q & A

What is the main topic of the talk?

The main topic of the talk is Counterfactual Fairness in machine learning, focusing on how to design algorithms that do not discriminate and are fair.

What are some examples of machine learning applications mentioned in the talk?

Examples mentioned include image classifications, human-level Atari and Go players, skin cancer recognition systems, predicting police officer deployment, deciding on jail incarceration, and personalized advertisements for housing, jobs, and products.

What issues are highlighted with machine learning systems in terms of fairness?

Issues highlighted include face detection systems that better identify white people, algorithms showing racist tendencies in advertising recommendations, and sexist biases in word embeddings associating men with bosses and women with assistants.

What is the intuitive notion of fairness proposed in the talk?

The intuitive notion of fairness proposed is that a fair classifier gives the same prediction had the person had a different race or sex.

How does the talk address the problem of sensitive attributes in machine learning?

The talk proposes a method that involves modeling the influences of sensitive attributes causally before constructing a classifier, using counterfactuals to determine what the classifier would predict had someone's race or sex been different.

What is the concept of 'Fairness Through Unawareness' mentioned in the talk?

'Fairness Through Unawareness' is a technique where sensitive attributes are removed from the classifier to make it unaware of these attributes, aiming to make fair predictions.

What is the issue with simply removing sensitive attributes in a classifier?

The issue is that the remaining features may still be influenced by the sensitive attributes, leading to biased predictions even though the classifier is unaware of the sensitive attributes directly.

What is the 'Equality of Opportunity' approach proposed by Hardt et al. in 2016?

The 'Equality of Opportunity' approach proposes building a classifier that uses sensitive attributes to correct for unfairness, ensuring equal accuracy in predicting outcomes like law school success for different racial groups.

How does the talk propose to model unfair influences in data?

The talk proposes modeling unfair influences by assigning variables for each feature, introducing causal links from sensitive attributes to these attributes, and using counterfactuals to determine predictions under different conditions.

What is the definition of 'Counterfactual Fairness' introduced in the talk?

'Counterfactual Fairness' is defined as a predictor being fair if it gives the same prediction in a world where someone had a different race, gender, or other sensitive attributes.

How does the talk demonstrate the practical application of the proposed fairness approach?

The talk demonstrates the practical application by using a dataset of US law school students, fitting a causal model, computing unobserved variables, and learning a classifier based on features that are not descendants of sensitive attributes.

What are the potential limitations or challenges in using the proposed fairness approach?

Potential limitations include the need for accurate causal models, the assumption that interventions are real, and the possibility that the model may not account for all biases, such as those in the selection of the dataset.

How does the talk address the trade-off between accuracy and fairness in machine learning?

The talk acknowledges that achieving counterfactual fairness may come at the cost of reduced accuracy, as some biased but predictive features are removed from the model.