MLOps Salon: Applying MLOps at Scale - Algorithmic Fairness: From Theory to Practice

Presented by: Ines Marusic - Engagement Manager at QuantumBlack, McKinsey & Company
Recent advances in machine learning have enabled us to automate decisions and processes across many specific tasks. Machine learning is increasingly being used to make decisions that can severely affect people’s lives, for instance, in education, hiring, lending, and criminal risk assessment. In these areas algorithms are used to make predictions for things such as job candidate screening, issuing insurances or making loan approvals. However, the training data often contains bias that exists in our society. This bias can be absorbed or even amplified by the systems, leading to decisions that are unfair with respect to gender or other sensitive attributes (e.g., race). The goal of fairness is to design algorithms that make fair predictions devoid of discrimination. I will discuss the recent advances coming from the machine learning research community on algorithmic fairness, including detection of bias in data, assessment of fairness of machine learning models, and post-processing methods for model predictions to achieve fairness. I will also provide a practitioner’s perspective through best practices of how to incorporate techniques from algorithmic fairness effectively on products in a variety of industries, including pharma, banking, and insurance.