Ethical AI Considerations (Bias, Fairness, Transparency)

Audience: professionals who use Claude at work, no coding or API knowledge needed. Verified = checked against Anthropic's documentation or help center on 2026-10-16. General background = standard regulatory or industry knowledge, not an Anthropic statement. Design guidance = widely taught practice.

Mostly design guidance and general background. Check Anthropic's Usage Policy and your organisation's AI principles for the exact rules that apply to you.

Bias

AI output can reflect skew in its training data, your documents and your prompts.

• Look for it: stereotypes, one-sided framing, missing perspectives, unequal treatment of similar cases.
• Test it: swap names, genders or regions in a prompt and compare outputs; ask for the strongest case on each side (D2.3).
• Do not use Claude alone to rank, screen or judge people.

Fairness

• Consider who could be disadvantaged by an output or a process built on it.
• Keep a human accountable for consequential decisions (jobs, credit, health, discipline, legal status) and offer a way to appeal or correct them.
• Apply the same standard to similar cases.

Transparency and honesty

• Be open that AI helped where people would reasonably expect to know (to customers, to colleagues whose work depends on it, in regulated settings) and follow your organisation's disclosure rules.
• Do not present AI output as verified or as human work when it is not.
• Do not use AI to deceive or manipulate people.
• Cite sources you actually checked; show the basis for important claims.
• State uncertainty and limits.

Respecting people and rights

• Protect privacy (D6.1).
• Respect intellectual property and licences; check the rights to material you feed in or publish.
• Avoid content that is harmful, discriminatory or illegal.

Reasoning through an ethical question (a simple method)

1. Who is affected (directly and indirectly)? 2. What could go wrong, and how serious and reversible is it? 3. Is it fair, honest and respectful of rights? 4. Is it allowed by law and policy? 5. Who is accountable, and is there human oversight? 6. Would I be comfortable explaining this use to the people affected? If any answer is uncertain, escalate to your manager, compliance or the AI governance owner.

Exam traps

• "The AI decided" as a justification.
• Using Claude to screen people with no human review.
• Hiding AI involvement where disclosure is expected.
• Treating bias as a technical issue only.
• No route for people to challenge outcomes.