Addressing Bias and Fairness in Machine Learning: A Practical Guide and Hands-on Tutorial
Abstract: As data science and machine learning (ML) increasingly shape our society, the importance of developing fair algorithmic decision-making systems becomes paramount. There is a pressing need to train data scientists and practitioners on handling bias and fairness in real-world scenarios, from early stages of a data science project to maintaining ML systems in production. Existing resources are mostly academic and cover the ML training and optimization aspects of bias mitigation, leaving practitioners without comprehensive frameworks for making decisions throughout a real-world project lifecycle. This tutorial aims to bridge the gap between research and practice, providing an in-depth exploration of algorithmic fairness, encompassing metrics and definitions, practical case studies, data bias understanding, bias mitigation and model fairness audits using the Aequitas toolkit. Participants will be equipped to engage in conversations about bias, assist decision-makers in understanding options and trade-offs, evaluate project scoping aspects influencing fairness outcomes, and define actions and interventions based on model predictions. They will also learn to identify cohorts, target variables, evaluation metrics, and establish bias and fairness goals for different groups. Moreover, participants will gain insights into auditing and mitigating model bias, and implementing continuous monitoring to assess retraining needs. The tutorial addresses the current lack of practical training materials, methodologies, and tools for researchers and developers working on real-world algorithmic decision-making systems. By the conclusion of this hands-on tutorial, attendees will be well-versed in navigating bias-related issues, selecting appropriate metrics, and applying bias audit and mitigation frameworks and tools for informed design decisions in real-world data science systems.
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