Ruth Yakubu

#MarchResponsibly with AI: Insights & Best Practices

1 | What is #MarchResponsibly?  March is known for International Women’s Day – but did you know that women are one of the under-represented demographics when it comes to artificial intelligence prediction or data for machine learning?  And did you know that Responsible AI is a key tool to ensure that the AI solutions of […]

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How to generate counterfactuals for a model with Responsible AI (Part 8)

A robust AI solution does not just make predictions but can also provide recommendations on how to change the model’s outcome. Data scientists and AI developers train and tune models to have the most optimal performance. However, for an AI system to be beneficial to decision-makers and end-users, it sometimes needs to be able to

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How to expose data biases from debugging your model with responsible AI (Part 6)

The traditional method of evaluating the trustworthiness of a model’s performance is to look at calculated metrics such as accuracy, recall, precision, root mean squared error (RSME), mean absolute error (MAE), or R2, depending on the type of use-case you have (e.g., classification or regression). Data scientists and AI developers can also measure confidence levels

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How to find model performance inconsistencies with Responsible AI (Part 5)

An effective approach to evaluating the performance of machine learning models is getting a holistic understanding of their behavior across different scenarios. One way to approach this includes calculating and assessing model performance metrics like accuracy, recall, precision, root mean squared error (RSME), mean absolute error (MAE), or R2 scores. However, just analyzing one metric

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Mission of Responsible AI

AI innovations are growing at a rapid pace. The breakthroughs are not just coming from large enterprises, there are amazing AI developments coming from startups or individuals as well. Regardless of how large or small the source is, one fact remains the same, AI systems do not always function as intended and have the potential

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How to perform Error Analysis on a model with the Responsible AI dashboard (Part 4)

Traditional performance metrics for machine learning models focus on calculations based on correct vs incorrect predictions.  The aggregated accuracy scores or average error loss show how good the model is, but do not reveal conditions causing model errors. While the overall performance metrics such as classification accuracy, precision, recall or MAE scores are good proxies

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How to create a Responsible AI dashboard to debug AI models (Part 3)

In the last tutorial, we trained a model, to predict diabetes patient hospital readmission, that we will be using to analyze and identify issues from the Azure Machine Learning’s Responsible AI dashboard. In this tutorial, we’ll learn how to create a Responsible AI (RAI) dashboard with its python SDK. We will show you how to

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How to train a machine learning model to be analyzed for issues with Responsible AI (Part 2)

When we train a machine learning model, we want the model to learn or uncover patterns. We focus on how accurately a model can make predictions and try to reduce the error rate of the model. However, by focusing too much on aggregated model performance metrics such as accuracy, we often neglect two important things

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