Crouching Harm, Hidden Bias—Addressing issues of fairness and bias in Machine Learning based decision making

Industries like retail, healthcare, finance, and even government provided services are being transformed by the application of Machine Learning (ML) techniques which can often find hidden patterns in data not always apparent to human experts. In spite of their benefits, the inferences drawn by these ML-based tools often have serious consequences for people, due to biases introduced at various points from training of ML models to deployment to production. With increasing attentions to such ML-based decisions from regulatory agencies, and consumer advocates, it is important for organizations to take into account issues of fairness and bias, before deciding to automate decisions with these systems. In this presentation, we explore the different kinds of biases that may be present in ML-based decision making, and what practical steps can be taken by organizations to reduce harm to individuals, and to comply with various regulatory frameworks.
Who is this presentation for?
Machine Learning practitioners, and decision makers.
Prerequisite knowledge:
Machine learning concepts
What you'll learn?
Addressing issues of bias and fairness in machine learning based decision makeing.

Ranjan is a technical leader and executive with experience in building and managing high-functioning teams, and deliver scalable and resilient enterprise applications. His expertise lies in building and architecting cloud-native, high-performance frameworks and applications, utilizing machine learning tools and techniques. Ranjan has worked at multiple start-ups and large organizations in the Boston area. He holds a BS in Electrical Engineering, and an MS in Computer Science from the Indian Institute of Technology, Kharagpur, India.