The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2023 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu .

Biased data is increasingly becoming a part of algorithms that deter- mine people's livelihood, such as predictive policing or recidivism predictors. One of the most effective ways of understanding how such algorithms work starts by examining the systems of oppression that lead to biased data. The lesson, "Intersectional Data Analysis: Examining Shootings in Boston", begins with examining the connection between racism, housing, and policing. Then, students use their data science skills to analyze how gun violence disproportionately harms African Americans. As a result, students examine the direct effects of historical bias embedded in data. The results show the student's ability to use data science and their knowledge of gun violence being a racial justice issue to create unbiased datasets, which may lead to fair algorithms.