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Center for Equitable Artificial Intelligence and Machine Learning Systems


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CEAMLS seeks to answer simple but impactful questions with its research such as, could an AI model cause harm, or could it perpetuate existing social hierarchies that prevent a fair playing field? Furthermore, the center facilitates the development of formal standards for data preparation, model training, and deployment to promote equitable AI. This is accomplished, in part, through the formalization of testing protocols for new AI innovations that promote the detection and mitigation of algorithmic bias.

CEAMLS is focused on 7 major areas in its research to clarify and guide AI development and implementation practices globally.

  1. Best Practices & Standards
  2. AI Transparency
  3. Objectivity Optimization
  4. Bias Detection & Remediation
  5. Testing & Validation
  6. AI Misapplication
  7. Model Stress Testing