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Explainable AI

Towards AI

Now, I’m not a fortune teller, but I’ve been in the AI landscape for a while now, and I can confidently tell you that these new trending AI technologies and applications will affect… Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. From research to projects and ideas.

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Explainable AI (XAI): The Complete Guide (2024)

Viso.ai

True to its name, Explainable AI refers to the tools and methods that explain AI systems and how they arrive at a certain output. Artificial Intelligence (AI) models assist across various domains, from regression-based forecasting models to complex object detection algorithms in deep learning.

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Building Trust in AI: The Case for Explainable Artificial Intelligence (XAI)

Pickl AI

Summary: This blog discusses Explainable Artificial Intelligence (XAI) and its critical role in fostering trust in AI systems. One of the most effective ways to build this trust is through Explainable Artificial Intelligence (XAI). What is Explainable AI (XAI)? What is Explainable AI (XAI)?

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10 Technical Blogs for Data Scientists to Advance AI/ML Skills

DataRobot Blog

With a goal to help data science teams learn about the application of AI and ML, DataRobot shares helpful, educational blogs based on work with the world’s most strategic companies. Explore these 10 popular blogs that help data scientists drive better data decisions. Read the blog. Read the blog. Read the blog.

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DataRobot Explainable AI: Machine Learning Untangled

DataRobot Blog

With any AI solution , you want it to be accurate. But just as important, you want it to be explainable. Explainability requirements continue after the model has been deployed and is making predictions. DataRobot offers end-to-end explainability to make sure models are transparent at all stages of their lifecycle.

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Explainability in AI and Machine Learning Systems: An Overview

Heartbeat

Source: ResearchGate Explainability refers to the ability to understand and evaluate the decisions and reasoning underlying the predictions from AI models (Castillo, 2021). Explainability techniques aim to reveal the inner workings of AI systems by offering insights into their predictions. What is Explainability?

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Generative AI vs. predictive AI: What’s the difference?

IBM Journey to AI blog

Explainability and interpretability Most generative AI models lack explainability , as it’s often difficult or impossible to understand the decision-making processes behind their results. Conversely, predictive AI estimates are more explainable because they’re grounded on numbers and statistics.