The adoption of Machine Learning in conjunction with traditional Decision Management is increasing 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 live together.
Similar integrations can also be achieved by using many different technologies, proprietary or based on open standards. For example DMN (Decision Model and Notation) and PMML (Predictive Modelling Markup Language) are well established standards for the representation of decisions and predictive models and work well together to enable a similar scenario.
In the past, another big barrier of entry for adoption was the cost and the complexity of similar highly data intensive applications; therefore only big enterprises already had the infrastructure to support them, while today’s cloud technologies (public, private and hybrid) make it possible for every company to leverage similar solutions.
Nowadays, the increased 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 presentation, attendees will have the opportunity to learn more about the Explainable AI, learn main concepts/definitions and see how they can be applied to Decision Managed and Hyperautomation solutions that run natively in the cloud.
Keywords: eXplainable Artificial Intelligence, Trustworthy, Machine Learning, Decision Management, DMN
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