Responsible AI in Action

This hands-on video shows you how to apply responsible AI toolkits to assess and interpret your machine learning models with explainability, fairness, error analysis, and what if (counterfactuals)scenario analysis dashboards using Microsoft Azure Machine Learning.

The GitHub repo for open-source Responsible AI toolbox:

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#ResponsibleAI #ExplainabaleAI #AzureMachineLearning #Azure #AI #DevOps #ML #XAI #Interpretability #FairnessAI #Fairness #Counterfactuals #AzureML

[myself]

🎯 Key Takeaways for quick navigation:

00:30 🕵️ *Lack of explainability in AI models can lead to trust issues, and understanding the root of errors is crucial for improvement.*
02:02 🛠️ *Azure Machine Learning is used as an IDE to showcase open-source responsible AI toolkits for assessing and visualizing model behavior.*
04:17 📊 *Error analysis toolkit helps identify conditions leading to errors, allowing for in-depth examination of specific scenarios.*
07:19 🔄 *Understanding feature importance and distribution assists in interpreting how the model utilizes different factors in making predictions.*
11:32 📉 *Feature importance can vary based on age, prompting consideration of whether such changes align with domain knowledge or indicate model correlation.*
12:55 🔄 *Individual prediction analysis and counterfactual scenarios provide insights into how specific changes in features affect model predictions.*
14:59 🔄 *What-if counterfactuals show the minimum changes required to flip predictions, offering a powerful tool for understanding model behavior.*
16:33 🧠 *Responsible AI toolkits support understanding model behavior, emphasizing the ongoing socio-technical challenge of ensuring responsible AI solutions.*

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[ocbhu]

This is great, really helpful but in my organization Azureml setup if I try to run this code - I can't access this url - could you suggest