Summary
This chapter focused on the best practices for designing explainable AI systems for industrial problems. In this chapter, we discussed the open challenges of XAI and the necessary design guidelines for explainable ML systems, considering the open challenges. We also highlighted the importance of considering data-centric approaches of explainability, IML, and prescriptive insights for designing explainable AI/ML systems.
If you are a technical expert, architect, or business leader responsible for using AI to solve industrial problems, this chapter has helped you to learn some of the most important guidelines for designing explainable AI/ML systems considering the open challenges in XAI. If you are a researcher in the field of AI or HCI, some of the open challenges discussed in the chapter could be interesting research topics to consider. Finding solutions to these challenges can lead to significant progress in the field of XAI.
In the next chapter, we will cover the principles...