Algorithmic Fairness and Social Ramifications

Date & Time: Wed., June 1, 2022 • 1–3 p.m. CT

Instructor:

• Theodora Chaspari | Texas A&M Computer Science & Engineering | chaspari@tamu.edu

Algorithmic Fairness and Social Ramifications

Learning Objectives:

• Identify terms and fundamental concepts related to fairness in AI.
• Identify methods to analyze bias and fairness in data.
• Identify methods to debias data.
• Identify methods to debias algorithms.
• Identify fairness evaluation metrics.

Recorded Session:

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