Gary Ackerman @garyackerman@qoto.org

IT affects both the nature of human brains and behaviors. The digital media landscape is much more participatory than the print-dominated one before it.

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

Humans are a technology-using species. Through IT, we extend our capacity to interact & manipulate the environment, being both social & technological at once,

Early electronic media like radio had inconsequential effects on classrooms, but modern digital tech is different, becoming the default for communication.

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

Education tends to adopt emerging skills into curriculum more slowly than other organizations.

Reflexive relationships exist between technology, the nature of information tasks, and interaction patterns.

The presence of researchers affects subject behavior, influencing observations made in social science studies.

“Ignorance more frequently begets confidence than does knowledge.” -Charles Darwin

It is so tiresome to read about genes “for” traits. Genetics does not work that way, except rarely.

If the job description calls for you to “multitask” make sure they mean “can manage multiple projects at the same time.” If they expect you to “work on multiple projects simultaneously,” then run!

“Experience is the name everyone gives to their mistakes.” -Oscar Wilde

While Galileo was a rebel, not all rebels are Galileo.
-Norman Levitt

If your hypothesis cannot be questioned—it is always correct despite the evidence—it cannot be called science.

"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

A significant source of bias comes from skewed or incomplete data sets used to train AI algorithms. This can lead to skewed outcomes. #DataBias

One way AI models become biased is through confusing correlation with causation. Two correlated factors changing together don't necessarily mean one causes the other. #DataBias

AI has demonstrated improved performance on tasks like image recognition, chess playing, and medical analysis, but biases persist. #AIDecisionMaking

Bias is an inclination toward or outlook that is prejudiced. In the real world, bias closely relates to discrimination or treatment. #AIBias