Category Archives: Explanations

Explaining Solutions of Combinatorial Optimization

Posted on April 30, 2025 by decisionmanagementcommunity

DecisionBrain is actively investigating the topic of explaining Combinatorial Optimization results. Drawing from principles in both Social Sciences and Artificial Intelligence, they highlighted two key types of explanations, contrastive and counterfactual explanations, and discussed their relevance in decision-support systems. Link

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Explainable Constraint Solving

Posted on September 28, 2023 by decisionmanagementcommunity

Explainable constraint solving is a sub-field of explainable AI (XAI) concerned with explaining constraint (optimization) problems. Although constraint models are explicit: they are written down in terms of individual constraints that need to be satisfied, the solution to such models … Continue reading →

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Intuit Tax Knowledge Engine

Posted on August 20, 2020 by decisionmanagementcommunity

Intuit just published a technical overview of their new Tax Knowledge Engine, the key innovation to make TurboTax smarter and more personalized for 37M+ consumers. First they listed key limitations for the traditional approach that are common for many rules-based … Continue reading →

Posted in Artificial Intelligence, Business Rules, Explanations, Human-Machine Interaction, Knowledge Representation, Logic and AI | Leave a comment

Explainability and Interpretability

Posted on December 9, 2019 by decisionmanagementcommunity

Explainability of decisions produced by machines is one of the hottest topic these days (see XAI). Explainable AI usually makes decisions using a complicated black box model, and uses a second (posthoc) model created to explain what the first model … Continue reading →

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How can we persuade people to trust an algorithm?

Posted on December 4, 2019 by decisionmanagementcommunity

On Dec. 4 Andrew Ng listed several important techniques  that can persuade people to trust an algorithm. “Trust isn’t just about convincing others that our solution works. I use techniques like these because I find it at least as important … Continue reading →

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DARPA Explainable AI Program

Posted on September 1, 2019 by decisionmanagementcommunity

Defense Advanced Research Projects Agency Explainable AI (XAI) program:

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IBM Research launches explainable AI toolkit

Posted on August 10, 2019 by decisionmanagementcommunity

IBM Research introduced AI Explainability 360, an open source collection of state-of-the-art algorithms that use a range of techniques to explain AI model decision-making. “That’s fundamentally important, because we know people in organizations will not use or deploy AI technologies … Continue reading →

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Explaining Decision Optimization Recommendations

Posted on March 30, 2019 by decisionmanagementcommunity

Explainability is a hot topic. Decision models are used to trigger recommendations such as “accept” or “refuse” a loan, but they also need to explain WHY they recommended certain decisions. Alain Chabrier from IBM DecisionOptimization team uses Portfolion Allocation problem … Continue reading →

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Statistical Methods with Domain-based Models

Posted on January 20, 2019 by decisionmanagementcommunity

This WSJ article gives examples when ML-based solutions have been enhanced by the inclusion of pre-defined domain-specific models. “Machine learning is a statistical modeling technique, which finds and correlates patterns between inputs and outputs without necessarily capturing their cause-and-effect relationships. … Continue reading →

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XAI – Explainable Artificial Intelligence

Posted on January 11, 2019 by decisionmanagementcommunity

Two new articles about explainable AI: Link1 Link2

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