How DMN could help to explain ML outcome

Dr Jan Purchase just published an article “Better AI Transparency Using Decision Modeling“, in which he describes several possible ways to provide reasonable explanations to the outcome of machine learning algorithms. Jan without hesitations describes practical limitations of the described approaches.  Jan and David Petchey will present two examples of their DMN-based approach to ML explanation at Decision CAMP 2018  that starts on Sep. 17 in Luxembourg. See also our post “Decision Automation and Explanations”.

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Launch of Dataset Search

Google launched Dataset Search, which can help researchers, scientists, and others around the world find open datasets! Dataset Search lets you find datasets wherever they’re hosted, whether it’s a publisher’s site, a digital library, or an author’s personal web page. The approach uses schema.org for describing datasets and anybody can publish their data following guidelines for dataset providers. Google encourages dataset providers, large and small, to adopt this common standard so that all datasets are part of this robust ecosystem. DM Community may utilize these capabilities for creation of publicly available Repository of Decision Models for different business domains. Continue reading

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Double-Axis Decision Tables

FICO Blaze Advisor 7.5 introduced the ability to create a double-axis decision table. Here is an example: Continue reading

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No future without maths

“Today’s and tomorrow’s jobs will require more and more mathematics; and yet the level of students in the West seems to be consistently dropping. What can we tech companies do about this? Maths seems to have worked its way into all fields, from financial markets to medicine, urban planning, transportation, and of course everything IT. Not only is this discipline at the heart of technological innovations that will transform the face of society; its teaching is also a major driver of growth – countries whose inhabitants are good at maths also tend to fare much better economically”, – writes Patrice Caine, Thales Chairman and CEO. Link

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Will we see Prolog’s resurgence?

Is Prolog the next good idea? Prolog, introduced in 1972, “was a short introduction to open doors to other languages than C, Java, Python or so. In a jungle of hundreds of programming languages, Prolog fits like a glove into the formalism of web standards and not just for the development of AI. For sure, Prolog deserves to be better known to have more developers embracing it, knowing that it can also be used as a complementary brick for more spreaded “leading” languages.” Link Continue reading

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Chaining Machine Learning and Optimization Models

Machine learning is about predicting, while optimization is about solving (searching for the values of variables that lead to the best outcomes). The latest Nathan Brixius’s post discusses different ways of combining optimization and machine learning:
1. Optimization as a means for doing machine learning
2. Machine learning as a means for doing optimization
3. Using the results of machine learning as input data for an optimization model.  Link

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Knowledge Graphs

Knowledge Graphs are becoming increasingly important to support decision and process augmentation based on linked data. “It’s all about things, not strings: A Knowledge Graph represents a knowledge domain. It connects things of different types in a systematic way. Knowledge graphs encode knowledge arranged in a network of nodes and links rather than tables of rows and columns. By that, people and machines can benefit from a dynamically growing semantic network of facts about things and can use it for data integration, knowledge discovery, and in-depth analyses.” Link

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Combining DMN and Blockchain

I consider combining DMN and blockchain as a very promising approach for a while. Early this year Edson Tirelli described how DMN decision services can be invoked from smart contracts. And today I read the first paper that addresses the execution of DMN-based decision models on an Ethereum blockchain dealing with collaborative decisions taken by multiple interacting business processes. The authors from the University of Potsdam attempted to transform DMN decision models expressed in S-FEEL to Solidity, the programming language of the Ethereum blockchain. Link Continue reading

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Decision Automation and Explanations

Prof. Gene Freuder wrote a position paper “Complete Explanations”: “The position taken here is that it can be worthwhile to start with truly complete explanations and abstract and limit from there. The goal is to provide a high-level “big picture” of the problem, in a form readily meaningful to a human user. The hope is that this may, as well, lead to general insights into constraint satisfaction problem structure… We need not just scalable algorithms but effective human-computer interfaces, including visualization tools, that help users grasp the big picture and explore their options.Link

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REALLY Smart (and Legal!) Contracts

In this article Dan Selman introduces “really” smart legal  contracts as well as the Open Source Accord Project. Dan will be among outstanding presenters during the upcoming DecisionCAMP-2018 in Luxembourg on Sep. 17-19.

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