Ethics in Machine Learning Systems
Ethics involves data acquisition and management and focuses on collecting data, with a particular focus on protecting individuals and organizations from any harm that could be inflicted upon them. However, data is not the only source of bias in machine learning (ML) systems.
Algorithms and ways of data processing are also prone to introducing bias to the data. Despite our best efforts, some of the steps in data processing may even emphasize the bias and let it spread beyond algorithms and toward other parts of ML-based systems, such as user interfaces or decision-making components.
Therefore, in this chapter, we’ll focus on the bias in ML systems. We’ll start by exploring sources of bias and briefly discussing these sources. Then, we’ll explore ways to spot biases, how to minimize them, and finally how to communicate potential bias to the users of our system.
In this chapter, we’re going to cover the following...
