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The other day, I was chatting with one of the members of our community. We started talking about the challenges she's facing with her project right now, and our exchange boiled down to the following question: "How can I find hidden biases in my data? Can you create a guide on how to do this with Hasty?"

So here we go. This post is a hands-on guide on how you can use Hasty's tooling to de-bias your data.

Bias in AI is a tricky thing; after all, you introduce the algorithm to biases in your data-set when you train an AI. So, how can you find harmful biases in your data that cause your models to break and may cause substantial damage?

The answer: you, as ML-engineer, need to understand how your models interact with the data and hunt the biases yourself. We're exploring some exciting approaches in the domain of active learning which might help in the future. But still, these approaches can only assist the human, never replace it. So I'm sorry; you need to put in some work.

To truly understand how your models interact with your data, you need to test, test and test your models under real-world conditions. With Hasty, you can do this for visionAI applications without long set-up processes and MLOps hustle.

To read the full article, please check out our Medium blog.