Summary
In this chapter, you learned about bias and ethical dilemmas that come from code, including LLM-generated code. This started with why it’s important to care about bias at all. We then saw some public embarrassments and troubles caused by biased code and other biased things. This chapter looked at detecting biases, measuring fairness, and preventing bad code generation in the first place. This involved getting balanced data, treating it fairly, checking comments, mentioning assumptions, documentation, widely used documentation, ethical coding standards, and code reviews done well.
There were links to helpful resources in this chapter. Finally, we looked at an example of LLM done well: not biased and also not too restrictive.
In Chapter 6, we’ll look at navigating the legal landscape of LLM-generated code. This will include unraveling copyright and intellectual property considerations, addressing liability and responsibility for LLM-generated code, examining...
