AI Ethics — AI Technical Interview Questions & Answers

AI Ethics

AI Ethics

  • AI ethics: fairness, privacy, accountability, transparency, safety. Who is blamed if a model denies a loan wrongly? Consent for training data. Don’t scrape private chats. A “code of ethics” is a checklist teams actually follow — not a poster.
  • Fresher example: a hiring model trained on past male-heavy hires. Accuracy can look high and still be unfair. Say fairness ≠ accuracy.
  • AI Ethics — output: harm / data / mitigation. Accuracy ≠ fairness.

Technical Interview Tip: One bias story + who is accountable.

Code Examples for AI Ethics

Example

# AI Ethics
print("harm       : who gets a worse outcome?")
print("data issue : bias or missing consent")
print("mitigation : audit / human review / better data")

AI Ethics — output: harm / data / mitigation. Accuracy ≠ fairness.

How to Explain AI Ethics to an Interviewer

Explain AI Ethics as if you are teaching a junior — definition, then one example.

Fairness, privacy, accountability. Accuracy ≠ fairness.

What does the example print, and what does that prove?

AI Ethics — output: harm / data / mitigation

What mistake do freshers make with AI Ethics?

Only the buzzword, no example