Bias Mitigation

The process of identifying and reducing systematic errors or prejudices in AI systems, datasets, and algorithms that lead to unfair outcomes for particular groups of people. In accessibility, bias mitigation is critical because AI training datasets often underrepresent people with disabilities, leading to models that perform poorly for or exclude these users. Strategies include diversifying training data to include people with various disabilities, auditing model outputs across disability groups, and involving disabled people in dataset creation and evaluation processes.