IFAN: An Explainability-Focused Interaction Framework for Humans and NLP Models We talked before about different techniques how to explain ML and NLP models. Ok, we have explained some model for a specific output, highlighted some tokens there. What should happen next? 📌You can use humans to debug and improve your model! What your steps can be: 1. 🔍You identify misclassified samples (for instance, during hate speech detection, you have noticed that the model is biased against some target words). 2. 📊You explain model's decisions and see that the models puts too much or too less weight/attention to some words. 3. 📝You edit the explanation, i.e. corresponding weights of the words spans that should contribute to the correct label. 4. 🔄You do this for several samples and retrain Adapter layer of your model based on new samples. 5. ✅Now your model's behavior is fixed, i.e. it is debiased! All this can be done with our platform: https://ifan.ml/ This is the first solid version, we are still developing many-many new features for it (as, for instance, the report page where you can control model's performance change). But already now, we believe that the platform can be a solid step to human-in-the-loop debugging of NLP models🤖. 📜The corresponding paper about this first version [link]