The TrustyAI‘s explainability library is primarily aimed at the Java Virtual Machine (JVM) and designed to be integrated seamlessly with the remaining TrustyAI services, adding explainability capabilities (such as feature importance and counterfactual explanations) to business automation workflows that integrate predictive models. Many of these capabilities are useful on their own. However, in the dataRead more →
In the second part of the blog series https://blog.kie.org/2020/12/how-to-integrate-your-kogito-application-with-trustyai-part-2.html we showed how to setup the OpenShift cluster that will host the TrustyAI infrastructure and the Kogito application we created in the first part https://blog.kie.org/2020/12/how-to-integrate-your-kogito-application-with-trustyai-part-1.html . In this third and last part of our journey, we are going to demonstrate how to deploy the TrustyAI infrastructureRead more →
Introduction to TrustyAI and Drools Explainability is a crucial aspect in modern AI and decision services work; recent laws entitle any person subject to automated decisions to explanations of the intuition behind said decisions. Moreover, people are more likely to trust the decisions of explained models compared to unexplained models (Kim et al., 2022). Furthermore,Read more →
In the first part https://blog.kie.org/2020/12/how-to-integrate-your-kogito-application-with-trustyai-part-1.html we have created a Kogito application and configured it to make it work with the TrustyAI infrastructure. In this second part, we are going to talk about the setup of the OpenShift cluster (https://docs.jboss.org/kogito/release/latest/html_single/#chap-kogito-deploying-on-openshift). The first step is to create a new project, which we call my-trusty-demo. As you canRead more →
Hi Kogito folks, In the previous blogposts we demonstrated how to deploy a Kogito service together with the TrustyAI infrastructure on an OpenShift cluster https://blog.kie.org/2020/12/how-to-integrate-your-kogito-application-with-trustyai-part-1.html.If you are new to TrustyAI, we suggest you read this introduction: https://blog.kie.org/2020/06/trusty-ai-introduction.html In this blogpost, we’d like to demonstrate how to get started with TrustyAI in ~15 minutes. In orderRead more →
How can you audit a decision out of your new Kogito application? It’s pretty simple: in this series of articles, we are going to demonstrate how to create a new Kogito application and how to deploy the TrustyAI infrastructure on an OpenShift cluster.If you are new to TrustyAI, we suggest you read this introduction: https://blog.kie.org/2020/06/trusty-ai-introduction.htmlWithRead more →
Recently, I’ve stumbled upon this interesting article and paired project about a Genetic Algorithm. Then, I’ve asked myself if somehow the features of Trusty PMML could be meaningfully used inside such context. I won’t go deep into technical details, but basically, the Genetic Algorithm classifies features as "genes", a set of genes is a "genoma",Read more →
As mentioned in the previous blog post we have three aspects that we are implementing: explainability, runtime and accountability. However we need to see how these are connected with the use cases and personas. The first aspect we will consider is runtime tracing and monitoring. The term monitoring refers to the system overseeing performance orRead more →
New to Kogito? Check out our “get started” page and get up to speed! 😉 This post presents the decision tracing addon: a component of the Kogito runtime quite relevant for the TrustyAI initiative (introduced here and here). One of the key goals of TrustyAI is to enable advanced auditing capabilities, which, as written inRead more →
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 →
