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TECH 113 W — Ethical Data and AI: Concepts and Tools for Responsible Decision-Making

Quarter: Fall
Course Format: Flex Online (About Formats)
Duration: 10 weeks
Date(s): Sep 26—Dec 9
Refund Deadline: Sep 29
Units: 2
Grade Restriction: No letter grade
Tuition: $545
Instructor(s): Angel Evan
Limit: 30
Class Recording Available: Yes
Status: Closed
Please Note: No class the week of November 21
DOWNLOAD THE SYLLABUS » (subject to change)
Flex Online(About Formats)
Sep 26—Dec 9
10 weeks
Refund Date
Sep 29
2 Units
Grade Restriction
No letter grade
Angel Evan
Please Note: No class the week of November 21
DOWNLOAD THE SYLLABUS » (subject to change)
How do we maintain ethics in a world awash in data? Problem-solving has grown increasingly complex as people, technology, and cultural events often blur the line between what is fair and what is violative. High-profile cases—such as the negative impact of Instagram’s algorithm on the mental health of teenage girls—illustrate what happens when data-driven initiatives go unchecked and are allowed to operate without ethical frameworks to help guide decision-making and outcomes.

This course goes beyond creating high-level awareness of data-related issues and focuses on making decisions in ethically ambiguous situations. It will provide current leaders and business professionals charged with making data decisions with a set of tools and decision-making capabilities for dealing with data-related ethical problems in real-world scenarios.

Students in the class are stratified into cohorts where they tackle four unique case studies: data governance and surveillance for the public good, questionable data encoded into machine learning models, data privacy and transparency issues for consumers, and ethics-related issues involving machines and robots. Each case study requires careful examination by the cohort and a commitment to a set of decisions based on a predetermined set of questions. By the end of the course, students will have learned how to make decisions in ethically complex situations involving data, which they can apply to their own organizations. No prior technical or statistical background is required.

Data Science Leader

Angel Evan led the data science team for the T-Mobile marketing solutions group. He has more than 10 years of machine learning experience and over 20 years of advertising and marketing experience. He studied data mining and analytics at UC San Diego and graphic design at Suffolk University.

Textbooks for this course:

There are no required textbooks; however, some fee-based online readings may be assigned.