Featured Posts: AI

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Getting started with TrustyAI in only 15 minutes
by Jacopo Rota

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Model fairness with partial dependence plots
by Tommaso Teofili

In this post we use PDPs to visualize potential fairness concerns in a machine learning model. More →

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A Genetic Algorithm with Trusty PMML
by Gabriele Cardosi

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. More →

Getting started with TrustyAI in only 15 minutes

by - May 26, 2021 AI General Article

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 →

Model fairness with partial dependence plots

by - May 4, 2021 AI Article

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 →

A Genetic Algorithm with Trusty PMML

by - March 15, 2021 AI Article

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 →

Predictions in Kogito: PMML endpoints with OpenAPI

by - February 25, 2021 AI Article

Introduction PMML is an XML standard whose scope is to define different kinds of predictive models (Regression, Scorecard, Tree, Neural Network, etc) in a system-agnostic way, so that it may be used and shared by different systems/implementations. The OpenAPI Specification (OAS) defines a standard, language-agnostic interface to RESTful APIs which allows both humans and computersRead more →

Event-driven decisions with Kogito

by - February 17, 2021 AI Rules Article

In 2021 it’s almost undeniable that modern application development needs to target the cloud, given the requirements of flexibility, scalability and availability imposed by today’s world. Event-driven architectures have proven to be well suited models for this purpose. As a result, we’re adopting these principles in several components of Kogito, which aims to be theRead more →

How to integrate your Kogito application with TrustyAI – Part 3

by - December 18, 2020 AI Article

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 →

How to integrate your Kogito application with TrustyAI – Part 1

by - December 7, 2020 AI Article

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 →

TrustyAI meets Kogito: the decision tracing addon

by - November 23, 2020 AI Article

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 →

An introduction to TrustyAI Explainability capabilities

by - October 29, 2020 AI Article

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 →

Finding counterfactuals with OptaPlanner

by - October 1, 2020 AI Article

Modern enterprises are increasingly integrated with Machine Learning (ML) algorithms in their business workflows as a means of leveraging existing data, optimising decision making processes, detecting anomalies or simply reducing repetitive tasks. One of the challenges with ML methods, especially with internally complex predictive models, is being able to provide non-technical explanations on why aRead more →