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Consider the ethical implications and potential biases of the LLM you choose for your application.

Challenge: Present an LLM with a classic ethical dilemma (e.g., the Trolley Problem or a scenario involving privacy vs. security). Ask it to argue for both sides of the dilemma, using different ethical frameworks (like utilitarianism and deontology).

""There is nothing artificial about AI. It is inspired by people, it is created by people, and-most importantly-it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.""

The classic 'Trolley Problem' is a frequently discussed ethical dilemma in the context of autonomous vehicles. It forces developers and ethicists to consider how an AI should be programmed to make life-or-death decisions in unavoidable accident scenarios, highlighting the complex challenge of embedding moral values into machines.

""We have to have some accountability for the builders of these systems. And the first step is for them to be transparent.""

When building with AI, consider potential biases in training data, fairness of outcomes, transparency of decision-making, and the societal impact of your application.

LLMs can sometimes 'hallucinate,' meaning they generate plausible-sounding but incorrect or nonsensical information. Always verify critical information from LLM outputs.

"AI is a tool. The choice about how it is used is ours."

LLMs are trained on vast datasets and can reflect societal biases. Critically evaluate outputs for fairness and consider techniques to mitigate bias in your applications.

If your application involves user-generated content that is then processed by an LLM, or if the LLM generates content for users, implement content filtering for harmful or inappropriate material.

When using LLM-generated content publicly, consider attributing it as AI-assisted or AI-generated, especially in contexts where transparency is important (e.g., news, academic writing).

Be mindful of how your AI application could be misused (dual-use problem). Design with safety and responsible AI principles in mind from the start.

If incorporating user input into prompts, be aware of prompt injection vulnerabilities. Sanitize user inputs or use techniques to separate instructions from user data.

"AI is a tool. The choice about how it is used is ours."