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

  • by Maurice
  • 32 minutes

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

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