Summary

This chapter provided an overview of the importance of developing appropriate governance frameworks for AI. The issue of automating bias in AI is a critical concern that requires urgent attention. Without appropriate governance frameworks, we risk exacerbating these problems and perpetuating societal inequalities. In this chapter, we outlined key terminologies such as explainability, interpretability, fairness, explicability, safety, trustworthiness, and ethics that play an important role in developing effective AI governance frameworks. Developing effective governance frameworks requires a comprehensive understanding of these concepts and their interplay.

We also explored the issue of automating bias and how the network effect can exacerbate these problems. The chapter highlighted the need for explainability and offers a critique of “black-box apologetics,” which suggests that AI models should not be interpretable. Ultimately, the chapter makes a strong case for the importance of AI governance and the need to ensure that AI is developed and deployed in an ethical and responsible manner. This is crucial to build trust in AI and ensure that its impacts are aligned with our societal goals and values.

The next chapter is upon us, like a towel in the hands of a galactic hitchhiker, always ready for the next adventure.