"Interpretability" in AI ethics is about understanding how AI systems make decisions. It's crucial for accountability, transparency, and identifying and addressing potential biases in AI algorithms. AI being right or fast is important but not the primary concern in this context.

The Transformation stage is where data discrepancies are often addressed. During transformation, data is cleaned, normalized, and validated to ensure consistency and reliability. This stage is critical for data quality and consistency in the data warehouse. Extraction involves collecting data, Loading is about data loading into the warehouse, and Data Ingestion is the process of bringing data into the system.

Federated learning trains machine learning models on decentralized data sources without transferring them to a central server. This approach is privacy-preserving and efficient. In contrast, traditional machine learning typically trains models on a single, centralized dataset, which may raise data privacy concerns.

Apache Spark provides the "GraphX" library for graph processing in a distributed environment. GraphX is a part of the Spark ecosystem and is used for graph analytics and computation. It's a powerful tool for analyzing graph data.

Pre-processing in computer vision typically includes steps like resizing, filtering, and transforming an image. It's during this phase that an image is converted into an array of pixel values, making it ready for subsequent analysis and feature extraction.

Recurrent Layers are not typically used in Convolutional Neural Networks. They are more common in Recurrent Neural Networks (RNNs) and are used for sequential data processing, unlike CNNs, which are designed for grid-like data.

In CNNs, the operation that combines the outputs of neuron clusters and produces a single output for the cluster is called "Pooling." Pooling reduces the spatial dimensions of the feature maps, making them smaller and more computationally efficient while retaining important features.

The healthcare database contains a mix of structured data (name, age, diagnosis) and semi-structured data (scanned documents and doctor's notes). Semi-structured data includes elements with partial structure, like documents, which can be tagged or indexed for better retrieval.

The model is likely facing the issue of overfitting. Overfitting occurs when the model learns the training data too well, including noise, resulting in excellent performance on the training set but poor generalization to unseen data. It's an example of a high-variance problem in the bias-variance tradeoff. To address overfitting, techniques like regularization and more data are often used.

MongoDB is a NoSQL database that is ideal for handling hierarchical data and provides better scalability for unstructured data. MongoDB uses BSON (Binary JSON) format, which makes it a good choice for applications that require flexibility and scalability in dealing with complex data structures.