What is model bias?
Quick answer
Model bias is a systematic error that makes an AI system perform worse for some groups than others, usually because the training data under-represented them.
Students evaluate a shared model against several test groups and propose one concrete data change that would narrow the gap.
In context
Bias is measurable. Split the test set by group, compare accuracy, and the disparity shows up in numbers a Grade 8 student can read.
Framing it as measurement rather than opinion keeps the conversation grounded and gives students something they can actually act on.
In a NASCA classroom
Students evaluate a shared model against several test groups and propose one concrete data change that would narrow the gap.
Related terms
Training data
Training data is the collection of examples a model learns from. Its size, balance and quality set the ceiling on how well the finished model can perform.
Machine learning
Machine learning is the part of AI where a model improves at a task by being shown labelled examples, rather than by being programmed with explicit rules.
AI ethics
AI ethics is the study of how automated systems should be built and used: who benefits, who is affected, what is disclosed, and who is accountable when the system is wrong.