AI is powerful. That power comes with responsibility. The first ethical question is the simplest: do I need AI for this? Can I do it myself? Being human-centered is not a limitation. It is a superpower. When you do choose to use AI, these principles help you use it with integrity.
AI systems make predictions based on patterns in data. They do not understand right and wrong. They do not have values. They do not feel consequences. That means every ethical decision about AI is a human decision. When you use AI, you are responsible for what you do with the output. When you build AI systems, you are responsible for who they affect and how.
AI ethics is not a separate subject. It is woven into every interaction you have with AI. Every prompt you write, every output you accept, every decision you make about AI use is an ethical choice.
AI should treat all people and groups equitably. If an AI system produces results that favor one group over another, that is a fairness problem that humans must fix.
People should know when AI is being used, how it makes decisions, and what data it was trained on. Hidden AI is a trust problem.
Someone is always responsible for AI outcomes. AI does not take blame. The people who build, deploy, and use AI systems are accountable for what those systems do.
AI systems often need data to work. That data should be collected with consent, stored securely, and used only for its intended purpose. Your personal information deserves protection.
AI systems should not cause harm. They should be tested, monitored, and corrected when they produce dangerous, misleading, or harmful outputs.
AI should support human decision-making, not replace it. Critical decisions about people's lives, education, health, and rights should always involve a human.
Bias in AI happens when the data used to train a model does not represent everyone fairly. If a dataset contains more examples of one group than another, the AI will learn to favor that group. This is not intentional. It is a reflection of the data.
Bias shows up in real-world AI systems. Hiring algorithms have favored certain demographics. Facial recognition has been less accurate for people with darker skin tones. Language models have reproduced stereotypes from their training data. Recognizing bias is the first step to addressing it.
When you evaluate AI output using the READY framework, the "A" step (Apply) is where you put your skills into practice. Ask: am I using AI correctly and safely? Am I following the guidelines for my school or workplace? Am I applying what I have learned about responsible use?
Using AI in school is not cheating. Using AI dishonestly is cheating. The difference is transparency and effort.
Honest AI use means disclosing when AI helped, using AI to learn rather than to skip learning, verifying AI outputs before submitting them, and putting your own thinking into the final product. AI can be a research assistant, a brainstorming partner, or a study tool. It should not be a ghostwriter.
The FOCUS framework teaches you to write better prompts. The READY framework teaches you to evaluate what comes back. Together, they give you a process for using AI with integrity.
Think through these situations. There are no easy answers. That is the point.
Before you use any AI output, run through these checks.
The FOCUS and READY frameworks give you a repeatable process for ethical AI use. Learn to prompt with precision. Evaluate with care. Own your decisions.
Explore FOCUS Framework