Machine Learning + Decision Management, a standards based approach (DEMO)

This is a demo extract from the talk: "Machine Learning + Decision Management, a standards based approach" presented at DecisionCamp 2019 BRAIN 2019 Bolzano Rules and Artificial INtelligence Summit 16-24 September 2019, Bozen-Bolzano, Italy

Abstract: Machine Learning (and AI in general) is not new, but has had a huge resurgence in the last few years, both in new investments and general adoption. The success of Social Networks and the increased availability of data collected by fully connected, always online systems, makes any solution that can make sense of such data very attractive. On the other hand, 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.
Decision Management on the other hand, is a discipline that typically aims to provide full transparency on the decision process, but requires translating knowledge into decisions/rules, using some form of knowledge engineering (automated or not).
However, there is a middle ground between these two approaches: combining decision models with analytic models is turning into an increasingly effective method to achieve a higher level of transparency, without losing effectiveness. Such approach achieves several of the goals of an Explainable AI (XAI), but still leverages abstract knowledge extracted from data for quality results.
While many technologies and frameworks exist to implement such strategy, a standards-based solution can be employed with several benefits over proprietary approaches. In particular, PMML (Predictive Modelling Markup Language) is a well established standard for the representation of predictive models, automatically generated from datasets using well known AI/ML technologies. DMN (Decision Model and Notation) is a Decision Modeling standard that provides out-of-the-box, transparent integration with predictive models.

This demo highlight how the combination of these two standards brings us an easy to use, high level, vendor neutral and effective solution for Explainable AI.

#MachineLearning #DMN #RHPAM #RHDM #Drools

[muhammadedwin]

hi Matteo, this is an interesting demo. Does the code is available on Github for reference?