How does bias in AI affect the Global South?
Overview
This lesson explores the impact of biased data on AI systems with a focus on the Global South. Students will learn how biased data leads to biased results since AI reflects the data it is trained on. In particular, students will focus on the impact of this on countries that are generally less industrialized and have lower income levels than developed nations in the Global North.
- AI & History-Social Science
- 60 minutes
Digital Materials
Objectives
After this experience, students will be able to
- Understand how bias in training data can affect AI outputs
- Identify examples of how underrepresentation and overrepresentation in AI data can lead to discriminatory outcomes
- Consider the impact of bias in AI on the Global South
Questions explored
- How can biased data lead to discriminatory AI outputs?
- What are real-world consequences of bias in AI data sets?
- What is the impact of bias in AI on less industrialized countries?
Key Terms
Algorithmic Bias
- When AI produces repeatable errors that create unfair outcomes, favoring some groups over others.
Artificial Intelligence (AI)
- The ability of computers to imitate human-like thinking, learning, and problem-solving.
Data
- Information collected together for reference or analysis. Often mentioned with computers and used to train many kinds of AI.
Machine Learning
- When computers learn and get better at a task by using data instead of being programmed with explicit rules on how to do that task.