Monday, June 18 | 1:50-3:05 PM
Level: Intermediate
Recommended Prerequisite: General understanding of modern data analytic methods (machine learning, etc.) used in decision-making
Field of Study: Auditing
Businesses, law enforcement and regulators rely on algorithmic decision-making to guide their activity, arriving at the optimal criteria for their choices via machine learning or similar methods. The use of computational methods is often celebrated for its freedom from the errors and biases that present themselves in human decision-making. This session will explore the truth of that perception, highlighting ways in which algorithmic decision making can produce results that reflect the biases of human cognition, or otherwise produce unfair results for minority populations and others.
You Will Learn How To:
- Recognize how prediction and decision-making algorithms work
- Identify sources of bias or feedback errors inherent in these systems
- Question the fairness of relying on automated results
- Examine the algorithms that you encounter at work