Discussing the article: "The Disagreement Problem: Diving Deeper into The Complexity Explainability in AI"
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Check out the new article: The Disagreement Problem: Diving Deeper into The Complexity Explainability in AI.
Dive into the heart of Artificial Intelligence's enigma as we navigate the tumultuous waters of explainability. In a realm where models conceal their inner workings, our exploration unveils the "disagreement problem" that echoes through the corridors of machine learning.
The disagreement is an open area of research in an interdisciplinary field known as Explainable Artificial Intelligence (XAI). Explainable Artificial Intelligence attempts to help us understand how our models are arriving at their decisions but unfortunately everything is easier said than done.
We are all aware that machine learning models and available datasets are growing larger and more complex. As a matter of fact, the data scientists who develop machine learning algorithms cannot exactly explain their algorithm’s behaviour across all possible datasets. Explainable Artificial Intelligence (XAI) helps us build trust in our models, explain their functionality and validate that the models are ready to be deployed in production; but as promising as that may sound, this article will show the reader why we cannot blindly trust any explanation we may get from any application of Explainable Artificial Intelligence technology.
Author: Gamuchirai Zororo Ndawana