Addressing Bias and Ethical Concerns in LLM-Generated Code

This chapter dives into the possible pitfalls of taking code from chatbots such as ChatGPT, Gemini, and Claude. The code may introduce bias, which can cause ethical problems. If you are aware that things might get tricky, you know to be careful and what to look out for.

Biases that might be hidden in code, even code generated by LLMs, include gender bias, racial bias, age bias, disability bias, and others. We shall get into those later in the chapter; see the Biases you might find in code and how to improve them subsection.

This chapter should help you manage your code more effectively and avoid taking things at face value. Here, you will be encouraged to think more carefully than a simple interpretation.

You’ll examine examples of unhelpful and wrong output from LLMs, consider what caused them to perform badly, and carefully consider your use of LLMs for coding. You’ll also learn how to avoid being unfair...