3. Scientific Integrity and Information Quality
Best practices include transparently articulating the strengths, weaknesses, intended optimizations or outcomes, bias mitigation, and appropriate uses of the AI application’s results.
7. Fairness and Non Discrimination
At the same time, applications can, in some instances, introduce real world bias that produces discriminatory outcomes or decisions that undermine public trust and confidence in AI.