Advances in AI techniques like machine learning and deep neural networks have potential to save time and boost productivity. But what if we train these technologies using datasets that exclude large portions of the population?
For example, some facial recognition software doesn’t acknowledge dark skin. Why? People of color were excluded from the datasets that were used to train the software. If AI isn’t designed with inclusion upfront, its rewards won’t equally benefit us all. The good news is that while such unconscious bias is unavoidable, it is not insurmountable. This talk will share:
What machine bias is
Why it’s dangerous for end users
The root cause of machine bias
How to add bias testing to product development lifecycles.