One way AI decisions become biased is by confusing correlation with causation. Just because two variables change together doesn't mean one causes the other. #AIFallacy
Newer pedagogical models call for students to be more active in defining curriculum, building knowledge, and communicating what they've learned. These emphasize complex problems & social interaction. #StudentCenteredLearning
Technology stewards are leaders who discover, invent, and share the practices for using IT to accomplish the logistic and strategic goals of the Communities of Practice. #TechLeadership
"Fairness" in AI can be measured in different ways, such as ensuring similar outcomes for individuals with similar qualifications ("individual fairness") or ensuring groups have proportional outcomes ("group fairness"). #AIFairness
Eliminating variables like race, gender, or origin from data doesn't automatically remove bias, as discrimination can still surface through correlations with other factors. #BiasMitigation
Director of Teaching and Learning Innovation at a community college in New England
Retired k-12 science/ math/ technology teacher/ technology integration specialist/ coordinator