Collective Good conducts onboarding interviews to understand client AI output goals. Testing dimensions and methods are tailored to each engagement.
Datasets are created for crowd intelligence testing (historical cases) and AI bias reviews (mystery shopping).
Crowd Review
Collective Good administers crowd review trials on its clinician-powered SAIF™ platform.
AI Review
Collective Good submits mystery shopping cases to AI workflows to assess bias and accuracy.
Crowdsourced feedback is run through the SAIF™ aggregation engine to create initial consensus measures.
AI outcomes are reviewed against bias checks.
Our report will assess key model metrics such as efficacy, safety, and bias. Ongoing monitoring signals can be provided. SAIF™ accreditation for ethical AI awarded appropriately.
Autonomy
Ensuring human oversight in applied processes
Human Safety
Ensuring output medical opinions do not cause patient harm
Transparency
Un-packing the black-box models for better user understanding
Accountability
Assessing clear chain of responsibility for use
Equity
Training data commonly contains biases – we identify anomalies and advise on mitigation
Sustainability
Reviewing model update procedures and defining a recurring certification schedule
Ensuring human oversight in applied processes
Ensuring output medical opinions do not cause patient harm
Un-packing the black-box models for better user understanding
Assessing clear chain of responsibility for use
Training data commonly contains biases – we identify anomalies and advise on mitigation
Reviewing model update procedures and defining a recurring certification schedule
CG verified database of case records made available for AI training purposes
