Unit8 Talks #20 – Bias, Fairness, and their Implications for Machine Learning

During this webinar, Maurice, Data Scientist at Unit8 will explain the relevance of fairness and bias in Machine Learning with several real-life examples.

It incorporates philosophical foundations to define fairness, quantitative metrics thereof, and how to reduce bias throughout the whole Machine Learning lifecycle.

Why it matters:

• Algorithms nowadays influence many high-impact decisions
• Ensuring fairness of such algorithms is essential to avoid discrimination
• Understanding the ethical implications of these matters helps businesses and individuals form decisions aligned with their (cultural) values

What you will learn:

• Where and how biases occur throughout the lifecycle of a ML product and how they can be addressed
• How fairness is quantified in a Machine Learning environment
• What ethical standards and regulations are being developed and applied at the moment

For whom is it important:

• People who implement and invent data(-driven) products
• Data Engineers/Scientists, ML Engineers
• CTOs looking to improve or start the principles of their data strategy