Here is a checklist to help you select a good partner for your “AI powered” Diversity projects:
- Be aware of your general use-cases and potential outliers
- Ensure your partner team of data scientists and data labelers is diverse
- Ask them what is the source of their data and make sure they combine inputs from multiple sources to ensure data diversity
- Question them about their data labeling. Do they have a gold standard, guidelines for their data labeling (A gold standard is a set of data that reflects the ideal labeled data for their task)
- Use multi-pass annotation for any project where data accuracy may be prone to bias. Examples of this include sentiment analysis, content moderation, and intent recognition.
- Find out if they have a data collection reviewing process in place
- Error tracking: Do they analyze their data regularly? Keep track of errors and problem areas so they can respond to and resolve them quickly
Hiring is the first step to add Diversity to your organization. Make sure you select the right partner to work with.