Innovation arcade: Exploration lab 
The Participants will:
Explore the ethical implications of AI
Discover equitable practices for AI usage
Explore culturally responsive CS history
Explore culturally responsive CS practices for students
Explore culturally responsive CS practices for teachers
Become equity advocates in their schools
Engage in hands-on challenge-based learning
Collaborate with other teachers
Converse about future-ready practices
Explore Historically Black Colleges and Universities as pathways to increasing diversity in computer science.
:00 Introduction: presenter background and past equity work examples of (technology creation across all curricular areas, ………)
:05 The Lens of Equity: We will look at the why of equitable AI & CS, examine common shortcomings in instruction, procedures to improve access, and connections to diverse pioneers.
:15 Equitable AI/CS Experiences: Participants will participate in project-based learning activities with some caveats that will challenge their thinking around equity of experiences. We will also engage collectively in short equity-focused lesson examples and connect them to broader real-world initiatives in which students can participate.
:55 Designing Student Experiences: Participants will be able to create their own equitable activities using templates and tools we provide.
:75 Share Out: Participants share out their creations.
:85 Wrap-Up: We will allow time for some questions and share resources the participants can use to continue their learning on digital accessibility.
https://www.geeksforgeeks.org/5-algorithms-that-demonstrate-artificial-intelligence-bias/
https://towardsdatascience.com/real-life-examples-of-discriminating-artificial-intelligence-cae395a90070
https://centerx.gseis.ucla.edu/computer-science-equity-project/student-voice/
https://www.nist.gov/news-events/news/2022/03/theres-more-ai-bias-biased-data-nist-report-highlights#:~:text=It%20is%20relatively%20common%20knowledge,particular%20gender%20or%20ethnic%20group.
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