This session explores the hidden biases and potential pitfalls of AI for educators. This session delves into the sources of bias in AI systems, how these biases can result in misleading information or reinforce harmful stereotypes, and their implications in educational settings where fairness and equity are critical. This session focuses on understanding, identifying, and mitigating bias in AI-generated outputs, providing practical strategies to ensure responsible and ethical AI use in the classroom. Leave equipped with the knowledge to become a more discerning and effective user of AI in education. Here are the session materials and other takeaways:
In addition, we have a
call to action for you - keep exploring AI tools and pushing these models while also identifying bias. When you are collaborating with AI, ensure to
ask yourself these three questions:
- Whose perspective shapes what we consider "acceptable" representation?
- How do our tools, from maps to algorithms, shape not just what we know, but how we think and the questions we ask?
- What voices are we amplifying, and which ones are we silencing?