“Why This Label?” — Explainability
It is the transparency screen showing exactly with the contribution of which word the machine learning model on your device gave that label to a news item.
When the system assigns a label, there is no black box behind it. When you press the ‘Why this label?’ button, you clearly see from which word the model received how many points while making that decision. As a requirement of the transparency invariant; the sum of the mathematical contributions of the words and the base value of the system (Σ(word contribution) + base = score) is always equal to the final score of the label.
Example: Seeing transparently on the screen that the word ‘earthquake’ in a headline provided a direct contribution of +0.42 points to the model.
Go deeper
Most modern deep learning systems hide their decisions behind inexplicable massive matrices. The lightweight (≈7 MB) classical classifier in MagPunk, on the other hand, is wholly explainable due to its linear features. This feature allows the user to question the algorithm’s decisions. Being able to see how the algorithm thinks (or how it errs) is the most effective way to keep in mind that they are statistical prediction tools, instead of blindly trusting artificial systems.
2.3.6 M6: Why This Label?
M6See how words contribute to the verdict score.