Category Archives: Responsible AI
Model Cards Example Machine Learning
Have you ever wondered how to make your machine learning models more transparent, understandable, and accountable? Are you looking to implement responsible AI practices including ways and means to review and improve your existing model documentation? If so, you will learn about the concept of model cards, a powerful tool for documenting important details about machine learning models. You will learn the concepts with concrete examples and best practices that can serve as a guide for implementing or improving model cards in your organizations. The model card example can be seen as an standard template for model card which gets used in various different companies such as Google. What are …
Lime Machine Learning Python Example
Today when core businesses have started relying on machine learning (ML) models predictions, interpreting complex models has become a necessary requirement of AI governance (responsible AI). Data scientists are often asked to explain the inner workings of a machine learning models for understanding how the decisions are made. The Problem? Many of these models stand out as “black boxes“, delivering predictions without any comprehensible reasoning. This lack of transparency (especially in healthcare & finance use cases) can lead to mistrust in model predictions and inhibit the practical application of machine learning in fields that require a high degree of interpretability. It could lead to erroneous decision-making, or worse, legal and …
Facebook Responsible AI: Lessons, Examples
As technology continues to advance, it’s important that we prioritize ethical considerations and ensure that the development and deployment of AI technologies are responsible and fair. Meta (formerly known as Facebook) recognizes the importance of responsible AI and has taken several steps to ensure that their AI systems are developed and deployed in an ethical and fair manner. In this blog post, we’ll be exploring the latest responsible AI updates from Meta, which every company should take into consideration when developing and implementing their own AI strategies and systems. I will keep the blog short and crisp. If you want greater details, visit this page. Use Varied Datasets & Robust …
I found it very helpful. However the differences are not too understandable for me