Courses / Assessment Methods & Selection
32. Bias Mitigation via Algorithmic Transparency
Practices to review and document how automated scoring or AI components reach decisions to spot bias risks.
Assessment design & talent acquisition
Bias Mitigation via Algorithmic Transparency
Documenting and inspecting how automated scoring or AI reach decisions so you can find and fix bias before hiring decisions are made.
"I need to trust that our automated test isn't silently favoring one group over another."
Algorithmic transparency means recording what data, features, and rules an automated scorer or AI model uses and providing understandable explanations of its decisions so bias risks can be detected and mitigated.
"I need to trust that our automated test isn't silently favoring one group over another."
Algorithmic transparency means recording what data, features, and rules an automated scorer or AI model uses and providing understandable explanations of its decisions so bias risks can be detected and mitigated.