Your Own Ethical Checklist as an AI Builder

You have spent lessons learning about bias, copyright, safety, alignment, and regulation. If you actually build and ship AI — even a personal project with a few users — all of it turns into concrete decisions you make at your keyboard. This lesson is the working checklist, not a lecture.

Before you build

1. 1Problem definition: what specific decision will your AI make? Who is affected?
2. 2Stakes: what happens to a person if it is wrong? Can they appeal?
3. 3Regulated domain check: is this hiring, credit, healthcare, law enforcement, education, migration? If yes, EU AI Act high-risk obligations plus local equivalents apply.
4. 4Data legality: do you have rights to the training/fine-tuning data? Did users consent to their data being used?
5. 5Alternatives: is a simpler non-AI solution actually better here?

While you build

1. 1Baselines: measure performance not just overall but across relevant demographic slices.
2. 2Ground-truth leak check: verify your test set is not in your training set.
3. 3Human-in-the-loop design: default to advising decisions, not making them.
4. 4Explainability: can you tell an affected user what the model considered?
5. 5Red-team pass: try to make it produce harmful outputs yourself before shipping.
6. 6Model card: write a one-page description of intended use, training data, limitations, evaluation results.

At launch

1. 1Transparency: users know they are interacting with AI (EU AI Act Art. 50).
2. 2Opt-outs: users can refuse AI involvement where it matters.
3. 3Incident channel: a clear way to report harms, with a real human reading it.
4. 4Kill switch: you can turn it off quickly if things go sideways.
5. 5Logging: enough to audit decisions after the fact, within privacy law.

After launch

1. 1Drift monitoring: performance on representative slices over time.
2. 2Feedback integration: real user complaints are the most valuable signal you have.
3. 3Retraining cadence: plan it, do not wait for a crisis.
4. 4Disclosure: publish periodic stats on decisions, overrides, and corrections.
5. 5Sunset plan: what happens when this product ends? Do users get their data back?

The ten questions to answer before you ship

Red flags that mean rethink, not ship

• You cannot explain why the model did what it did on a failure case
• Evaluation was only done on the group you expected to use it
• Opt-out exists only in a cookie banner
• Monitoring plan is 'we'll check back in a quarter'
• Stakeholder consultation was limited to your team
• The business model depends on users not fully understanding what is happening

What this does NOT mean

• Refuse to build. Most AI is net positive when built well.
• Add friction for its own sake. Bad UX is not ethics.
• Endless committee approval. Speed matters; so does rigor.
• Treat users as hostile. Assume good faith, design for the edges.
• Chase every new framework. Pick one solid one and apply it consistently.

“Ethics is not a checklist you pass. It is the thing you do between the checklist items, when nobody is watching.”

The big idea: AI ethics as a builder is not philosophy. It is a set of engineering decisions, made early, revisited often. The builders who think about these questions at the start ship better products. The ones who do not end up in a headline they did not want. You choose which story you want to be part of.