Why XAI is an important ?

Why XAI is an important ?

Just developing a ML model and producing some decent accuracy is not enough. Explanations of AI models have the potential to make our AI systems more trustworthy and it is an important for business value. There are important areas every AI Engineer should focus for good business enablement.

Responsible AI -> Explainable AI (XAI)

XAI which is powerful concept can apply for AI which in turns pursuit of converting black box models into transparent and interpretable algorithms.

(The user of the black box model can understand the AI results but cannot see the logic behind them)

Main benefits of XAI

Transparency <=> Interpretability <=> Explainability

Practical implementation through some python libraries

• ?SHAP (SHapley Additive exPlanations) https://shap.readthedocs.io/en/latest/index.html
• ?Shapash? - https://shapash.readthedocs.io/en/latest/
• LIME - https://github.com/marcotcr/lime
• DALEX (moDel Agnostic Language for Exploration and eXplanation) - https://github.com/ModelOriented/DALEX
• TreeSHAP - https://github.com/linkedin/fasttreeshap

There are many packages already available to achieve the XAI.

Finally, Model Agnostic Explanations is driving best model strategy for building strong AI and responsible AI for business.

The more that you read, the more things you will know!

Raja Mahendra