Avoiding Gender Bias in AI
In our last data blog post, we discussed how there are gender biases in AI. But, we didn’t get to the part where we told you how to avoid these gender biases.
Here are the five best practices we have found:
- You want to make sure that you have diversity in your training samples. Use as many female audio samples as you do for males. Keep it fair and keep it diverse, which means considering data with a representation of gender variants such as transgender, non-binary, etc. This will expand the tool’s understanding of how to handle expanding diversity.
- When labeling the audio samples, make sure that the people in your team come from diverse backgrounds so you don’t have a skewed labeling system.
- Make sure that your team measures accuracy levels separately for the different demographic categories so that they can identify if a category is being treated unfairly.
- The more information your dataset has and the bigger your dataset is, the better.
- Collect training data from all groups of people. Once you do this, you can use modern machine learning de-biasing techniques that offer ways to identify errors and also catch unfairness.
The machine-learning and AI field will keep growing and though these are some steps you can take to help diminish the biases within AI, there is a whole lot more work that needs to be done. Holistic approaches need to be taken to address the causes of bias we discussed in the previous blog post.
Our society has an obligation to create technology that is fair and useful for everyone and it’s up to the leaders and practitioners of the field to develop solutions that help reduce bias in AI for everyone.