Vince Nelidov
Vince Nelidov is a Staff Data Science at Blue Yonder with diverse consulting experience in the data domain in a variety of industries from energy sector and banking to skincare and agriculture. Throughout his years in the data world, Vince has been combining advanced data science with business insights to make data work with an impact. He aspires to see far beyond what is on the surface and get to the essence of the problems, discovering robust and scalable long-term solutions rather than temporary fixes.
Vince is passionate about sharing his knowledge and insights, believing that Data literacy should not be a privilege of a few. And his goal is to be there to make this a reality. Making the intricacies of data science intelligible and uncovering the regularities hiding in the data is a major source of inspiration for Vince. With this goal in mind, he combines his years of experience in consulting with his background in statistics, research and teaching to make this knowledge accessible to businesses and individuals in need.
Session
Bias in machine learning models remains a pressing issue, often disproportionately affecting the most vulnerable groups in society. This talk introduces a Bayesian perspective to effectively tackle these challenges, focusing on improving fairness by modeling and addressing bias directly.
You will learn about the interplay between uncertainty, equity, and predictive accuracy, while gaining actionable insights to improve fairness in diverse applications. Using a practical example of a risk-scoring model trained on data with underrepresented minority groups, I will showcase how Bayesian methods compare to traditional techniques, demonstrating their unique potential to mitigate bias while maintaining performance.