5 Ways To Avoid Artificial Intelligence Bias With 'Responsible AI' - Liwaiwai

To promote consistency, accountability, and transparency, organizations should document findings, such as the origins and gaps in the training data used in AI systems. Documentation should include populations who are under or overrepresented for whom the AI system may have different success rates. This will put on notice those testing for gaps and harms at later stages as well as downstream users of the AI systems. This practice is akin to adhering to nutritional labels and ingredient lists, such as AI model cards, that documents what and who is part of datasets. We may not like them, but we have routine dentist visits.