Trustworthy Decision Management: How Explainable, Predictive Decision Making Can Help Us Trust Our Decision Models


The adoption of Machine Learning in conjunction with traditional Decision Management has increased over the last few years: user data can be easily collected and processed so it is crucial now to leverage similar information to build Intelligent Applications where Machine Learning and Decision Management combine.
Similar integrations can also be achieved by using many different technologies, proprietary or based on open standards. Red Hat embraces open source and open standards and the Red Hat Decision Manager platform offers the possibility to use DMN (Decision Model and Notation) and PMML (Predictive Modeling Markup Language) standards to represent decisions and predictive models well integrated together.

Nowadays, a modern decision automation solution cannot limit to modeling and execution aspects because the demand for transparent, explainable decision making, that is accurate, consistent and effective, has never been greater. Legislations like GDPR are just a result of increasing concerns about privacy, safety and transparency in general. While AI/ML solutions are great at making sense of high volumes of data, the reasoning process for most of the generated analytic models is usually quite opaque.

Explainable AI is a research field that aims to make Machine Learning models more transparent and explainable. In reality the same approaches/techniques can be generalized and applied to Decision Automation solutions in general to provide insights and increase the trustworthiness of the system.

During this session, attendees will have the opportunity to learn more about the Trustworthy Decision Management concept and see how it can be applied to Decision Managed and Hyperautomation solutions that run natively in the cloud.


Jacopo Is a Senior Software Engineer at Red Hat, where he contributes to TrustyAI development and other Red Hat Cloud platform services. Jacopo graduated in Computer Science at the University of Milan with a thesis on the regret minimization for reserve prices in Second-Price auctions.
Before joining Red Hat, Jacopo worked for one of the largest reinsurance companies on a white label telematics product for the motor insurance market distributed worldwide.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from - Youtube
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google