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