AI Fairness

Chapter 1. Understanding and Measuring Bias with AIF 360

Bias can occur at any stage in the machine learning pipeline (Figure 1-1), and fairness metrics and bias mitigation algorithms can be performed at various stages within the pipeline. We recommend checking for bias as often as possible, using as many metrics as are relevant to your application. We also recommend integrating continuous bias detection into your automated pipeline. AIF360 is compatible with the end-to-end machine learning workflow and is designed to be easy to use and extensible. Practitioners can go from raw data to a fair model easily while comprehending the intermediate results, and researchers can contribute new functionality with minimal effort.

In this chapter, we look at current tools and terminology and then begin looking at how AIF360’s metrics work.

Figure 1-1. Bias in the machine learning pipeline

Tools and Terminology

Several open source libraries have been developed in recent years to contribute to building fairer AI models. Most address only bias detection, not mitigating bias. Just a handful of toolkits (like Themis-ML and Fairness Comparison) address both, but they are often limited for commercial use due to their usability and license restrictions. IBM fairness researchers took on the initiative to unify these efforts, as shown in Table 1-1, and bring together a comprehensive set of bias metrics, ...