Show Notes
In this episode of Trailblazing Schools, Trudy explores the biases inherent in AI and large language models, drawing parallels with historical biases in publishing and education. She emphasises the importance of understanding these biases to use AI responsibly and ethically.
Trudy discusses the impact of bias on marginalised communities, strategies for recognising and mitigating bias and the importance of diverse training data to reduce bias in AI. Finally, she highlights the role of education in addressing bias and leaves the listener with some practical questions to reflect on.
Resources:
Find out more about the Trailblazing Schools Partnership here: https://www.trudygraham.com.au/trailblazing-schools/
Find out more about the Leaders Learning Circle here https://www.trudygraham.com.au/leaders-learning-circle/
Get your copy of AI in Action: Field Notes from School Leaders. It's free, practical, and designed to prompt your own next step. Download today at trailblazingschools.com/fieldnotes
If this conversation resonated and you're exploring how to strengthen leadership in your school network or system, visit trudygraham.com.au/connect to explore working together.
Credits
The Trailblazing Schools Podcast is hosted by Trudy Graham - https://www.trudygraham.com.au/
This podcast is edited and produced by Ellen Ronalds Keene from PERK Digital - https://perkdigital.com.au/
The theme music is Dusty Road by Indiebox
Trudy Graham acknowledges the traditional custodians of the land on which she lives and is recording today, the Darumbal people, and pay my respect to elders past and present, for they hold a deep and continuing connection to the land, sea, and sky.
Chapters
00:00 Introduction: Bias in AI and Education 01:56 Historical Bias in Data and Publishing 03:55 Representation Bias and Its Consequences 05:00 Publication Bias and Its Impact on Knowledge 06:07 Naming AI to Foster Critical Thinking 07:07 Bias Beyond Representation: What's Not Seen 08:04 Historical Power and Marginalized Voices 09:02 The Matilda Effect and Gender Bias 10:00 Bias in Publishing and Its Legacy 10:58 Practical Steps to Recognize Bias in AI 12:09 Encouraging Curiosity and Critical Inquiry 12:57 Closing Thoughts: The Role of Education and Inquiry
AI bias, large language models, representation, historical bias, education, ethical AI, diversity in publishing, bias in training data
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