Have you ever used a machine learning (ML) algorithm and been confused by its predictions? How did it make this decision? AI-infused systems are increasingly being used within businesses, but how do you know you can trust them? We can trust a system if we have confidence that it will make critical business decisions accurately.Read more →
In this blog post you’ll learn about the TrustyAI explainability library and how to use it in order to provide explanations of “predictions” generated by decision services and plain machine learning models. The need for explainability Nowadays AI based systems and decision services are widely used in industry in a wide range of domains, likeRead more →
In this article, we introduce the metrics monitoring add-on for Kogito. This add-on is part of the TrustyAI initiative already introduced in the previous article https://blog.kie.org/2020/06/trusty-ai-introduction.html . Like Quarkus extensions, the Kogito add-ons are modules that can be imported as dependencies and add capabilities to the application. For example, another add-on is the infinispan-persistence-addon thatRead more →
In previous posts (PMML revisited and Predictions in Kogito) we had a glance at how a PMML engine has been implemented inside Drools/Kogito ecosystem.This time we will start looking at a concrete example of a recommendation engine based on top of PMML.The first part of this post will deal with the ML aspect of it,Read more →
In a previous post (Shopping recommendations in PMML) we built the main infrastructure for making personalized shopping recommendations depending on the customer shopping history. The companion project was meant to demonstrate a standalone implementation of the Trusty-PMML engine. In this post, we will show how to invoke the engine on a remote Kogito instance. KogitoRead more →
Sometimes the result of an automated decision may be neither desired or that which was required. What if there was a tool to find a way to overturn those decisions, maybe changing some of the figures that were provided to the system, and achieve a different outcome? That’s what we’ve been working on lately withinRead more →
A quick guide on how to leverage partial dependence plots to visualize whether an ML model is fair with respect to different groups of people. As machine learning models, and decision services in general, are used more and more as aiding tools in making decisions that impact human lives, a common concern that is oftenRead more →
In a previous blog post we have discussed how to leverage LIME to get more insights about specific predictions generated by a black box decision service. In fact LIME is mostly used to find out which input features where most important for the generation of a particular output, according to that decision service. Such explanationsRead more →
Following up from a previous blog post about integrating Drools with the Open Prediction Service, in this new post we want to share the current results from another exploration work: this time integrating Drools with research on Transparent Machine Learning by IBM. Introduction Transparency is a key requirement in many business sectors, from FSI (FinancialRead more →
When working with decision services, it’s often hard to understand the rationale behind the output for a given prediction. A noteworthy example is the one related to having a decision service for loan approval denying the loan request to a given user. The user would surely like to know the rationale behind such a denial.Read more →
