| Type | Title | Author | Replies | Last updated |
|---|---|---|---|---|
| Assignment | Breaking the Code: Understanding Algorithmic Bias | shallma1 | 0 | 7 months 3 weeks ago |
| Assignment | Rise Against the Machines: Understanding Algorithmic Bias | shallma1 | 0 | 1 year 7 months ago |
| Assignment | Database Scavenger Hunt | shallma1 | 0 | 2 years 3 months ago |
Algorithms are everywhere, and they have increasing power over what we consume (Amazon, Netflix, TikTok), who we date (“the apps”), and how we understand the world (Google, ChatGPT). So, what are algorithms, and how did they become so powerful? Who are the humans that create them, and why does it matter?
In this workshop, we will explore how algorithms can perpetuate bias and discrimination, and discuss some preventive strategies. It is open to learners of all backgrounds and experience.
Description: The Database Scavenger Hunt engages pairs of students in locating specific information or performing specific tasks across multiple resources. Each team works through a series of 16 questions/tasks, with verification of correct answers from the librarian/professor after every 1 or 2 questions, then places a mark on the corresponding wall grid of questions once an answer is deemed correct. The process repeats until the team completes all questions.
Algorithms are not neutral but this does not mean they are not useful tools for research. In this workshop on algorithmic bias, student learn how algorithms can perpetuate bias and discrimination and how to critically evaluate their search results.
Reading charts and infographics is part of everyday life, yet telling a story with data can be tricky. Luckily, data visualization is a skill that everyone can learn! Data visualization is the practice of translating information into a visual context, helping humans understand complex concepts and making it easier to identify patterns and uncover insights. In this workshop, learn the basics of designing data visualizations, selecting appropriate graph styles, and how to identify misleading data visuals.
