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TrustyAI SHAP: Overview and Examples

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SHAP is soon to be the newest addition to the TrustyAI explanation suite. To properly introduce it, let’s briefly explore what SHAP is, why it’s useful, and go over some tips about how to get the best performance out it. A Brief Overview Shapley Values The core idea of a SHAP explanation is that ofRead more →

An introduction to TrustyAI Explainability capabilities

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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 →

TrustyAI meets Kogito: decision monitoring

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In this article, we introduce the metrics monitoring add-on for Kogito. This add-on is part of the TrustyAI initiative already introduced in the previous article https://blog.kie.org/2020/06/trusty-ai-introduction.html . Like Quarkus extensions, the Kogito add-ons are modules that can be imported as dependencies and add capabilities to the application. For example, another add-on is the infinispan-persistence-addon thatRead more →

Shopping recommendations in PMML.

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In previous posts (PMML revisited and Predictions in Kogito) we had a glance at how a PMML engine has been implemented inside Drools/Kogito ecosystem.This time we will start looking at a concrete example of a recommendation engine based on top of PMML.The first part of this post will deal with the ML aspect of it,Read more →

Shopping recommendations in PMML – Kogito at Work

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In a previous post (Shopping recommendations in PMML) we built the main infrastructure for making personalized shopping recommendations depending on the customer shopping history. The companion project was meant to demonstrate a standalone implementation of the Trusty-PMML engine. In this post, we will show how to invoke the engine on a remote Kogito instance. KogitoRead more →

Counterfactuals; getting the right answer

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Sometimes the result of an automated decision may be neither desired or that which was required. What if there was a tool to find a way to overturn those decisions, maybe changing some of the figures that were provided to the system, and achieve a different outcome? That’s what we’ve been working on lately withinRead more →

Model fairness with partial dependence plots

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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 →

Local to global – Using LIME for feature importance

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In a previous blog post we have discussed how to leverage LIME to get more insights about specific predictions generated by a black box decision service. In fact LIME is mostly used to find out which input features where most important for the generation of a particular output, according to that decision service. Such explanationsRead more →

Effective data generation for explaining decision services

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When working with decision services, it’s often hard to understand the rationale behind the output for a given prediction. A noteworthy example is the one related to having a decision service for loan approval denying the loan request to a given user. The user would surely like to know the rationale behind such a denial.Read more →

Transparent ML, integrating Drools with AIX360

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Following up from a previous blog post about integrating Drools with the Open Prediction Service, in this new post we want to share the current results from another exploration work: this time integrating Drools with research on Transparent Machine Learning by IBM. Introduction Transparency is a key requirement in many business sectors, from FSI (FinancialRead more →