Category Archives: Explanations

Explainability and Interpretability

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?

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

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

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IBM Research launches explainable AI toolkit

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

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

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

Two new articles about explainable AI: Link1 Link2

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