Gary Ackerman @garyackerman@qoto.org

Error causes mistakes in both directions.
Bias causes mistakes in one direction.

Data can be messy, incomplete, and inconsistent. Cleaning it up is a real pain.

If you do not find this to be true, then you need to reevaluate your data.

My "reset your passwords" links from vendors have surpassed my SPAM emails.

Task-switching leads to poorer learning and performance.

The sources challenge the assumption that today's students, immersed in technology, are naturally adept at using it for learning. While they may be comfortable with technology for social and entertainment purposes, research shows their skills in information literacy, critical evaluation, and effective knowledge construction are often lacking.

There are reasons software is so difficult to use. We are using someone else's version of what needs to be done to accomplish things.

The idea that students are "digital natives" who are naturally good with technology is a myth.

Many people believe in learning styles, but research shows they don't exist.

Creativity, collaboration, and critical thinking are the new ABCs! Tech-driven curricula prioritize 21st-century skills over rote memorization.

Yet Another Curriculum Repository Proposal
https://buff.ly/401E0tZ

On Innovation
https://hackscience.education/2024/12/18/on-innovation/

Think of it like a three-legged stool: tech advances, market needs, and adaptable organizations. All three are vital for innovation success.

Innovation isn't just about cool tech! It's the magic that happens when tech, markets, and organizations collide.

Education and awareness are crucial. We need to educate ourselves and others about the potential risks of AI bias.

Bias embedded within the design or implementation of an algorithm, leading to unfair or prejudiced outcomes.

The ability of a computer or machine to perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.

Moral principles that guide human behavior and decision-making, particularly in the context of technology development and deployment.

If the data used to train an AI algorithm reflects existing societal biases, the AI will learn and perpetuate those biases.

While eliminating all bias in AI is challenging, significant progress can be made through ongoing research, development, and implementation of bias mitigation techniques. The goal is to minimize bias and ensure that AI systems are fair, accountable, and beneficial to society.

Trust in AI can be fostered by:
Making AI systems understandable and their decision-making processes explainable.
Establishing clear responsibility for the outcomes of AI systems.
Ensuring that AI systems are designed and used in a way that is equitable and just.
Regularly assessing AI systems for bias and taking steps to mitigate any identified issues.