Ethics for Data Scientists


In recent years we have seen a rise in the use of artificial intelligence in our everyday lives: from machine learning algorithms that screen resumes for job postings, to bots that can converse with one another. In this workshop, participants will explore the pitfalls of artificial intelligence when it comes to algorithms responsible for making decisions impacting lives, and what our responsibilities are to ensure our models are ethical.

Learning outcomes

By the end of this session, participants will be familiar with

  • An overview of different frameworks data scientists use when modelling or abstracting.
  • Various types of pitfalls data scientists can fall into when modelling or abstracting a problem using machine learning techniques.
  • Concrete examples of what these pitfalls look like in practice, with a focus on consequences on parties affected by algorithms.
  • An introduction to Fairlearn: a Python-based library that helps data scientists improve fairness in machine learning and artificial intelligence systems.
Zachary Shand
Zachary Shand
Data Scientist