Blog Details

SHAP-Explainable AI for Model Interpretability

Rory Donovan
June 21, 2023

Over View

Artificial Intelligence (AI) has made incredible strides, but understanding how and why AI systems make decisions is still challenging. That’s where SHAP (SHapley Additive exPlanations) comes in. This innovative approach to explainable AI enables users to uncover the intricate workings of AI models and develop ethical systems that are explainable, equitable, and reliable.

In this blog post, we’ll explore the significance of SHAP-explainable AI for model interpretability and how it promotes transparency in AI development, all in a way that’s easy to understand.

01 Create a topic for discussion

Draft a document that contains a question you would like answered.

02 Schedule a consultation

Select the date and time that works best for you.

03 Talk to an expert

Get the virtual assistance you need to succeed in your domain.

What is SHAP

SHAP-explainable AI (also known as SHapley Additive exPlanations) is an open-source algorithm that enables model interpretability by providing visual explanations of individual predictions. The framework explains how each feature contributes to the prediction, making it easier to identify areas of potential bias in automated decision-making systems. 

This makes SHAP a powerful tool for detecting unfairness and improving transparency in machine learning models.

Demystifying AI with SHAP

Artificial intelligence (AI) models are often considered enigmatic or “black boxes” operating in mysterious ways. However, breakthroughs such as SHAP provide a framework for understanding how these models work. SHAP utilizes game theory principles to assign a contribution to each feature, making the decision-making process more transparent. 

By analyzing individual features’ impact, SHAP provides advanced insight into the decision-making process of AI models.

Understanding the Logic of SHAP

The SHAP approach assigns a score to each feature, called the Shapley value, by assessing its significance in the prediction process. This value depicts the average contribution of a feature in collaboration with other features. In simple terms, it measures the impact of each feature when combined with various others in the prediction process.

To calculate the Shapley value, SHAP considers all possible permutations of features and evaluates their contributions. For each permutation, it systematically determines the marginal contribution of each feature by comparing the model’s predictions when the feature is included or excluded.

Get Free Access to Ethical AI with Our IaaS Platform

Ethics play a pivotal role in the development and deployment of AI systems. By signing up for the Infrastructure-as-a-Service (IaaS) platform, users gain access to a powerful toolset.  The IaaS platform offers an intuitive interface that allows users to develop AI models and leverage SHAP for interpretability effortlessly.

Collaborate with Us

By signing up for the IaaS platform, users gain access to a valuable resource that enables them to harness the power of SHAP. Get in touch with us so we can collaborate on a project or so you can join our expert advisory network.

Contact Our AI Consulting Firm

At VirtuousAI, our team of experts is here to provide you with the expertise and guidance needed for implementing successful models that leverage transparent interpretation tools. Contact us today for more information about how our services can help you create interpretable models that meet your business requirements. 

Request a FREE demo today!


Leave a Reply

Your email address will not be published. Required fields are marked *