Explainable AI

8 Explainable AI Frameworks

Explainable AI te ayuda a comprender los resultados que genera el modelo de aprendizaje automático predictivo para las tareas de clasificación y regresión definiendo cómo cada atributo de una fila de datos contribuyó al resultado previsto. A menudo, esta información se conoce como atribución de atributos.

Entre los frameworks mencionados se destacan:

What-if Tool: TensorFlow team announced the What-If Tool, an interactive visual interface designed to help visualize datasets and better understand the output of TensorFlow models.

LIME: Local Interpretable Model-Agnostic Explanations LIME is an actual method developed by researchers at the University Of Washington to gain greater transparency on what’s happening inside an algorithm.

DeepLIFT: DeepLIFT is a method that compares the activation of each neuron to its ‘reference activation’ and assigns contribution scores according to the difference.

AIX360: The AI Explainability 360 toolkit is an open-source library developed by IBM in support of interpretability and explainability of datasets and machine learning models.

Activation Atlases: Google in collaboration with OpenAI, came up with Activation Atlases, which was a novel technique aimed at visualising how neural networks interact with each other and how they mature with information along with the depth of layers.

44 Repositorios en github sobre explainable ai

Incluye: DALEX, AIX360, LIME, xai, DiCE, cxplain.

An overview of model explainability in modern machine learning

Towards a better understanding of why machine learning models make the decisions they do, and why it matters