Machine learning models often perpetuate biases that exacerbate societal inequities, particularly for vulnerable groups. As machine learning increasingly shapes critical decisions, addressing these biases is more important than ever. In this talk, I will explain how Bayesian methods offer a principled and effective approach to improving fairness by directly addressing bias and incorporating uncertainty into machine learning models.


The talk will cover:

1. Theoretical Foundations: I will start by exploring the connection between Bayesian statistics, fairness, and accuracy, with a focus on why uncertainty is a crucial factor in fairness interventions.
2. Practical Example: Using a risk-scoring model trained on a dataset with underrepresented minority groups, I will demonstrate how Bayesian methods compare to traditional fairness techniques. This example will illustrate their ability to not only mitigate bias but also adapt to complex, real-world data distributions while maintaining predictive accuracy.
3. Key Insights and Applications: Finally, I will provide actionable takeaways on incorporating Bayesian thinking into existing workflows, enabling more equitable and robust outcomes across diverse applications.


This talk is designed to be accessible to a broad audience. While minimal familiarity with machine learning concepts and fairness principles is recommended, no advanced knowledge of statistics is required. Attendees will leave with practical tools, code examples, and insights to address bias effectively in real-world scenarios, empowering them to promote fairness in their own projects and organizations.