Explainable AI for Practitioners

Chapter 6. Advanced and Emerging Topics

with Sheeraz Ahmad

The focus of the book so far has been on well-established techniques, modalities, and use cases. However, Explainable AI (XAI) continues to be an active area of research, so that new techniques are continually being developed and existing techniques are improved and scrutinized further. Feature-based explanations such as Shapley values and Integrated Gradients introduced in the previous chapters can cover many use cases, especially as applied to text, tabular, and image data. However, there are several emerging techniques and topics that can be valuable in your explainability toolbox in specific situations.

In this chapter, we will discuss three broad, emerging topics. First, we will introduce alternative explanation techniques like attribution to inputs (as opposed to features) and making models explainable by design. Second, we will briefly cover how some of the previously introduced techniques can be more generally applied to data formats that are not text, tabular, or image, specifically focusing on time-series and multimodal data (text + image). Third, we will discuss how explainability techniques can be evaluated in a systematic way, as opposed to spot checks on a handful of data points.

Alternative Explainability Techniques

In this section, we will discuss two alternative explainability methods, namely, alternate input attribution, which is attribution to training data points or user-defined concepts, as well as ...