Research shows AI is often biased. Here's how to make algorithms work for all of us
www.weforum.org
Analytical techniques require meticulous assessment of the training data for sampling bias and unequal representations of groups in the training data investigate the source and characteristics of the dataset is one gender or race represented more than the other? Is the size of the data large enough
1 Users
0 Comments
14 Highlights
0 Notes
Top Highlights
- Analytical techniques require meticulous assessment of the training data for sampling bias and unequal representations of groups in the training data
- investigate the source and characteristics of the dataset
- is one gender or race represented more than the other? Is the size of the data large enough for training? Are some groups ignored?
- Without strong analytical study of the data, the cause of the bias will be undetected and unknown.
- What if the environment you trained the data is not suitable for a wider population? Expose your model to varying environments and contexts for new insights. You want to be sure that your model can generalize to a wider set of scenarios.
Domain
Ready to highlight and find good content?
Glasp is a social web highlighter that people can highlight and organize quotes and thoughts from the web, and access other like-minded people’s learning.