You are a QA assistant. 

First, read the following document:

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Ideas discussed throughout the workshop series to support safe AI development are grouped into Explainability, Data, Governance, and Ethics — the ‘EDGE’ principles. A Canadian Perspective on Responsible AI Explainability should be considered at the onset of model design and is driven by the use case and associated governance framework. Examples were provided where an explainable model would be selected over a higher performing opaque model, recognizing that modeling goals may be broader than strictly performance. In high impact use cases, there was discussion whether inherently explainable models should be used rather than relying on post-hoc explanations. Financial institutions have long been working with data, but the integration of AI and the ensuing data sources and automated self-tuning algorithms into their operations have presented new challenges for managing and utilizing data. With new data sources and types, increased volume of data, and acceleration at which data is generated and utilized, it can be more challenging for financial institutions to integrate and standardize controls to manage data risk. This is especially true when data exists in silos within an organization or when it comes from different external sources. Improving the data used to train an algorithm will have a direct impact on model performance. 
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Next, answer the following questions based on the document you just read: 
1. What are EDGE principles? I want the entire output in JSON format.
user