Computing systems contribute to systemic bias. This is something we talk about but students are not typically taught how to examine bias in systems or make conscious design decisions that prevent bias that once coded will persist undetected in systems for a long time.
In this course, students are taught how to examine bias in systems.
We will be combining data analysis, data visualization, web development, software development tools and open government data to design, build and analyze systems that allow for civic engagement and explore data bias, systemic bias and social justice related issues in the local community or national community.
This course will use Python as the primary language and will examine data in the following formats: csv, json, xml, data from apis, etc.
Class Meeting time: 2pm - 4pm M-W-F online on zoom.
What should I be working on?: Course assignments will be linked off the schedule below. Submitted in Sakai. Grades available on Sakai.
| Week # | Week of | Monday | Wednesday | Friday |
|---|---|---|---|---|
| 01 | Jan. 04 | 01 - Introduction, Types of cognitive bias - Slides | 02 - Systemic bias and technology | 03 - Data exploration |
| 02 | Jan. 11 | 04 - Identifying bias in computing systems - Slides | 05 - Data and community - Local and national data sources | 06 - Designing systems for community engagement |
| 03 | Jan. 18 | 07 - Data cleaning - Slides | 08 - Data transformation | 09 - System and visualization building |
| 04 | Jan. 25 | 10 - Viz /UI assessment - Slides | 11 - Final system presentations | End of winter session semester. No class. |