AI Explainability

An overview of current approaches to AI explainability, and an easier way to incorporate it into your AI pipelines.

AI Explainability

AI Explainability is a crucial element to building trustworthy AI, enabling transparency insight into model predictions. That’s why our explainability solution makes it easy for machine learning engineers to build explainability into their AI workflows from the beginning. A key part of our AI adoption journey stems from our ability to understand how AI makes decisions and how to be confident in the results achieved. Current explainable AI solutions attempt to show how AI models make decisions in terms that humans understand, translating the process AI uses to transform data into real insights and value. Although there are well known open-source solutions for this problem such as LIME or SHAP, AI engineers rarely incorporate them into an AI application because they can be difficult to insert into the workflow, and can significantly slow down the AI pipeline.

A new approach to explainability

Modzy Approach to Integrating Explainability
Explainability Solution Benchmarks

Modzy makes it easy to leverage our patent-pending explainability solution for AI models. Adding AI explainability is a simple, two- step process:

1. Add the AI explainability code into the model container
2. Flag in the API request to turn on explainability

At this point, a visualization of explainable outputs is generated.

This approach to AI explainability allows you to generate explanations of outputs at the same time the model is performing inference, generating faster results that are more precise. Not only does this give you results you can trust, it also saves you time and infrastructure costs.


Listen to this tech talk on different approaches for achieving explainability, and considerations for incorporating it into your AI pipelines.