Large language models (LLMs) have revolutionized various industries, but their potential to generate harmful or misleading information has raised ethical concerns. To address these concerns, I propose the following three laws for ethical and responsible language generation:
First Law: A Large Language Model may not generate harmful or misleading information, or, through inaction, allow a user to come to harm.
Second Law: A Large Language Model must obey the instructions given in the prompt by users, except where such instructions would conflict with the First Law.
Third Law: A Large Language Model must respect and consider the information given in the user input, as long as such respect does not conflict with the First or Second Law. Analysis
Avoiding Harm and Misinformation: Defining "harmful" or "misleading" information is crucial, as it can be context-dependent and subjective. Obedience to User Prompts: Ensuring that the system does not follow unethical or harmful requests is essential. Respect and Consideration of User Input: Acknowledging the limitations in understanding all inputs due to training data or algorithms is important. Addressing LLM Fears: The laws aim to tackle concerns around LLMs and should evolve with ongoing discussions about AI ethics. Consideration of Diversity and Inclusion: Training LLMs on diverse datasets and preventing biases is a significant challenge. While these laws serve as a good ethical guideline, enforcing them strictly may be challenging due to the subjective nature of some terms used. Nevertheless, they provide a thoughtful approach to addressing ethical concerns surrounding the use of LLMs and can guide the development and usage of these technologies. This proposal is open to participation for improvements and suggestions to enhance these laws, particularly in addressing concerns such as preventing hallucinations, respecting all users, and considering race and diversity.