3 credits
Fall 2025 Lecture Upper DivisionData from last updated 10/3/2025
Fall 2025
Fall 2025 Instructors:
This course examines advanced topics relating to algorithmic fairness, transparency, and interpretability of data-driven decision-making systems. Topics include the data science lifecycle, data quality, algorithmic bias, debugging and mitigating bias, and transparency of data and algorithms.
Learning Outcomes1Recognize current issues related to the transparency and fairness of decisions made by data-driven decision-making systems.
2Examine sources of unexpected and discriminatory behavior of data-driven decision-making systems.
3Contrast existing methods and design novel techniques to mitigate undesired system decisions.
Prerequisites
One of
Restrictions
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