Introduction
In today's data-driven world, the demand for machine learning models that not only excel in accuracy but also adhere to ethical principles has never been more pronounced. The Responsible AI dashboard provides data scientists and AI developers with the essential tools necessary to craft machine learning models that prioritize societal well-being and inspire trust. This dashboard empowers us to confront crucial concerns like discrimination, inclusiveness, transparency, and fairness in machine learning. Traditional machine learning model evaluation metrics frequently fall short in identifying responsible AI issues, encompassing fairness, inclusiveness, reliability/safety, privacy & security, accountability, and transparency. Practical tools like the Responsible AI dashboard are instrumental in comprehending the societal impact of your AI model and, most importantly, how to improve it to be less harmful.
In the lab later in this module, we'll be using the UCI hospital diabetes dataset to train a classification model using the Scikit-Learn framework. The model will predict whether or not a diabetic patient will be readmitted back to a hospital within 30 days of being discharged.
Learning objectives
In this module, you've learned how to:
- Create a responsible AI dashboard.
- Identify where the model has errors.
- Discover data over or under representation to mitigate biases.
- Understand what drives a model outcome with explainable and interpretability.
- Mitigate issues to meet compliance regulation requirements.