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DDS8530

Module 4 Required Resources

When Computers Decide

Larus, J. et al. (2018). When Computers Decide: European Recommendations on Machine-Learned Automated Decision Making [PDF] 

  • The page 11 of this report discussed ethical considerations of machine learning decisions. This also applies when labeling data as machine learning models mimics what we labeled to predict on unseen data. 

Overview of Getting Inferences on Vertex AI 

Google Cloud. (2025). Overview of Getting Inferences on Vertex AI.

  • This document explains the two main methods of inferences in Vertex AI: online and batch. Then, it goes on to show how to get inferences from custom trained and AutoML models. The page also contains a tutorial with Python Notebooks at the end on how to perform inferences in Vertex AI. 

Cloud Composer Overview 

Google Cloud. (2025). Cloud Composer Overview. 

  • This document explains how to use Google Cloud’s cloud composer which is based the famous Apache Airflow. In Airflow, workflows are created using DAGs (Directed Acyclic Graphs) and this guide shows how you can create DAGs for your project. 

Module 4 Optional Resources

Transparency in Algorithmic Decision Making

Koszegi, S. T. (2019). Transparency in Algorithmic Decision Making [PDF] 

  • This special edition from ERCIM discusses various topics related to transparency in algorithmic decision making. While most of the topics covered are very important, the sections on ethics are very relevant to this module. Specifically, “Ethics in Research” in page 4, “How to Include Ethics in Machine Learning Research” in page 5, and “Transparency in Algorithmic Decision Making” in page 10.