How do AI chatbots like ChatGPT generate its results? Here's what you need to know about the Explainable AI (XAI) & the use of Graph Technology behind it!

Published in AGEDB , 3 min read, Jun 1

Graphs describe AI results effectively

With the growing attention to ChatGPT, the technology that gives the most appropriate answer to the questions human ask, Machine Learning and Artificial Intelligence are becoming hot industry topics. Among the users, however, some may be wondering why and how it generates the results. And if you are one of those who are wondering, now you know why your math teachers demanded solving equations to your definite answer!

XAI (eXplainable Artificial Intelligence) is a technology that would explain the results of AI at the human-understandable level. This article will explain the mechanics of how AI generates the results (XAI), the use of Graph Technology, and the benefits it delivers in this area.

Source: DARPA (Defense Advanced Research Projects Agency)

So why are Graph Databases great tool for creating XAI models?

Well, in short, Graph Databases help model relationships between complex data. Now, let's take a look at different use cases.

Application in various areas

One of the use cases of XAIs modeled using Graph Databases is in the research field, discovering molecular structure and interaction prediction. Modelling molecular structure and interaction prediction. Modeling molecular structure and interactions is critical in developing new and existing drugs. Graph Databases enable the storage of molecules' structures and interactions for analytical purposes. Machine Learning models will then use these data to make explainable (human-understandable) predictions of molecule behaviours.


Graph Databases are also widely employed in Finance and other industries that require predictive models. Graph Databases can model and interpret relationships among various account transactions. Providing results of predictive models with explanations through graphs reassures the precision of the forecasting results for financial analysts who then will be providing financial services for customers.

Lastly, Graph-based XAI is also commonly used in the medical field. An example use case is storing patients' diagnosis information, test results, and treatment data and their analysis. Doctors can predict patients' diseases and suggest treatment methods via graph databases. The help of Machine Learning models provides doctors with explainable results to choose the treatment options plans, and most importantly, it presents doctors with extensive insights into each patient faster and more accurately than a human brain is capable.

Graph Database for effective XAI implementation

Why do people use AI?-To predict challenges we may face in the future and gain appropriate solutions to these specific problems that average human brains cannot deduce. By storing and learning information beyond our capabilities through AI, we can eliminate the cause of the problems and and apply AI-generated solutions to our needs.

And why are Graph Databases needed?-Today, humans are still more likely to trust the given results when they are understandable. DFOr users to comprehensively understand the AI-generated results, Graph Databases that can easily model the relationship between complex data will be essential.

Graph Analytics supports the next evolution of Machine Learning by enhancing its capabilities for learning Graph patterns and providing explainable models and results. When combined with AI and ML, it can be a powerful combination that will benefit organizations for years to come.

AGEDB 's Graph technology extension service also provides businesses with Graph technologies that can be combined with AI/ML.

Ready to see the potentail use of Graph technologies in your areas of expertise? Contact us today to unlock the future an extension away with our Graph Data extension capabilties!

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