AI Assurance

1. An introduction to AI assurance<br>2. Setting the goals for ethical, unbiased and fair AI<br>3. An overview of explainable and interpretable AI<br>4. Bias, Fairness, and assurance in AI: Overview and Synthesis<br>5. An evaluation of the potential global impacts of AI assurance<br>6. The role of inference in AI: start S.M.A.L.L. with mindful models<br>7. Outlier detection using AI: a survey<br>8. AI assurance using casual inference: application to public policy<br>9. Data collection, wrangling and preprocessing for AI assurance<br>10. Coordination-aware assurance for end-to-end machine learning systems: the R3E approach<br>11. Assuring AI methods for economic policymaking<br>12. Panopticon implications of ethical AI: equity, disparity, and inequality in healthcare<br>13. Recent advances in uncertainty quantification methods for engineering problems<br>14. Socially responsible AI assurance in precision agriculture for farmers and policymakers <br>15. The application of AI assurance in precision farming and agricultural economics <br>16. Bringing dark data to light with AI for evidence-based policy making