My Journey with Azure Machine Learning Responsible AI Components
(Part 1)
By Manu Bhardwaj
As the importance of ethical considerations in artificial intelligence (AI) and machine learning (ML) continues to grow, the concept of Responsible AI has become an essential topic. I embarked on a learning journey to explore Azure Machine Learning’s Responsible AI (RAI) components, which offer a suite of powerful tools to develop responsible AI models. In this post, I’ll share my experience and key takeaways. I would like to express my gratitude to Ruth Yakubu for her insightful blog post “Getting started with Azure Machine Learning Responsible AI components (Part 1),” which provided valuable guidance.
The Responsible AI (RAI) Dashboard: An Integrated Experience
The RAI Dashboard is a core component of the Responsible AI Toolkit, a suite of tools designed to provide a customized, responsible AI experience with unique and complementary functionalities. The capabilities are accessible either as open-source tools on GitHub or through the Azure Machine Learning platform.
What sets the RAI Dashboard apart is its ability to seamlessly integrate various responsible AI capabilities, facilitating deep-dive investigations without the need to save and reload results in different dashboards. The dashboard brings together features such as model statistics, data explorer, error analysis, model interpretability, counterfactual analysis, and causal inference. It is built on leading open-source tools, including ErrorAnalysis, InterpretML, Fairlearn, DiCE, and EconML.
Getting Started: Creating an Azure Machine Learning Workspace
The tutorial began with a walkthrough on creating an Azure Machine Learning workspace, which is the foundational step for training models and creating RAI dashboards. Ruth provided detailed prerequisites, including the steps to create an Azure ML workspace and navigate to the Azure Machine Learning Studio. Here, I was able to access several features to complete end-to-end ML lifecycle tasks.
Verifying RAI Insights Components
After creating the workspace, the tutorial guided me on how to verify the RAI components within Azure Machine Learning Studio. By navigating to the “Components” tab and searching for “RAI” in the system registry, I could view the list of different RAI components.
Hands-On Exploration: Diabetes Hospital Readmission Dataset
With the workspace set up, Ruth introduced the Diabetes Hospital Readmission dataset, which is publicly available. The dataset would be used to illustrate how to apply each of the RAI components for analysis and debugging. I gained insights into tools like InterpretML, Fairlearn, DiCE, and EconML, and how they contribute to responsible decision-making, fairness assessment, counterfactual analysis, and causal inference.
Conclusion and Next Steps
My exploration of Azure Machine Learning’s Responsible AI components has been a rewarding experience. I am eager to continue my journey with the next tutorial to further enhance my understanding of responsible AI practices. I highly recommend exploring the resources provided by Azure AI and the wider AI community.
I would like to extend my thanks to Ruth Yakubu for her valuable insights and guidance. For more information, please refer to the following resources:
Key Insights Gained from the RAI Components
- InterpretML: InterpretML provides access to state-of-the-art glassbox models (interpretability models) and helps interpret opaquebox ML models. It answers questions like “what factors influenced the predictions the most?” and “does my model satisfy compliance and audit requirements?”
- Fairlearn: Fairlearn explores fairness by assessing model performance across groups defined by sensitive attributes (e.g., age, gender, ethnicity). It identifies disparities and provides mitigation algorithms to address observed fairness issues.
- DiCE: DiCE generates counterfactual datapoints, helping ML engineers and decision-makers explore alternative model outcomes. For example, a loan officer can provide recommendations to change a loan denial prediction based on counterfactual analysis.
- EconML: EconML explores causal relationships to answer questions like “how would holidays impact revenue?” and “what if we change product pricing?” It helps expose potential positive or negative outcomes from actions.
Conclusion and Anticipation for Part 2
My exploration of Azure Machine Learning’s Responsible AI components has expanded my knowledge of ethical considerations in AI and ML. I am excited to continue this journey and eagerly anticipate Part 2 of the tutorial, where I’ll have the opportunity to train a model and create an RAI dashboard.
Once again, I extend my gratitude to Ruth Yakubu for her valuable insights and expertise. I encourage others to embark on this journey toward responsible AI development using the resources provided by Azure AI and the broader AI community.
