Wrap‑up and next steps

Estimated time: 20 minutes

One‑page checklist: Reproducible, FAIR, and ethical ML in astronomy

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Use this as a quick self‑check for your next ML project. You do not need to tick every box to start. Ticking a few is already progress.

1. Reproducibility (can someone rerun this?)

• I can rerun my own main result today
• Code is versioned (e.g. Git, tagged release)
• Randomness is fixed or recorded (seeds noted)
• Train / validation / test splits are explicit
• Preprocessing steps are documented
• Software environment is described (versions or environment file)

Minimum goal: someone else could rerun this without guessing.

2. Data provenance (where did this come from?)

• Data source is stated (archive, survey, release)
• Selection criteria are described
• Any filtering or exclusions are documented
• Derived features are explained
• Labels or annotations are described

Minimum goal: a reader understands what data went in, and why.

3. FAIR practices (is this reusable?)

Findable

• Outputs are named clearly
• A README explains what is in the repository

Accessible

• Access conditions are stated (open, restricted, embargoed)
• Instructions for access are included if needed

Interoperable

• Standard formats are used where possible
• Units and conventions are stated

Reusable

• A short description of intended use is included
• Limitations are stated explicitly

Minimum goal: reuse does not require emailing you.

4. Models and outputs (what exactly is being shared?)

• Training data are described
• Preprocessing assumptions are stated
• Intended scope of the model is documented
• Known failure modes are listed
• Outputs include uncertainty where relevant

Minimum goal: users know what the model was built for.

5. Ethical safeguards (are assumptions visible?)

• Model scope is stated clearly
• “Not tested on” cases are listed
• Performance claims are not overgeneralised
• Automation is not treated as ground truth
• Language avoids implied universality

Minimum goal: downstream users are not misled by omission.

6. The five‑minute test

If you stopped working on this today:

• Could you explain the project in one page?
• Could someone rerun the main figure?
• Would limitations still be obvious?

If not, write one sentence to fix that.

Remember

• Clarity beats completeness
• Partial reproducibility beats silence
• Small improvements compound

You are not aiming for perfect practice.
You are aiming for fewer mysteries.

Wrap‑up and next steps

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This workshop has focused on a simple idea:

Machine learning does not change what good astronomy looks like.
It changes how easy it is to lose track of assumptions.

Across the lessons, we have seen that:

• reproducibility is a practical skill, not a moral stance
• FAIR practices extend beyond raw data to models and workflows
• ethical issues in astronomy ML are usually about scope and reuse
• small, explicit choices prevent large downstream problems

None of these require perfect code or ideal infrastructure. They require clarity.

What you should take away

If you remember only a few things:

• You are the primary user of your own ML work
• Performance numbers are not self‑explanatory
• Models outlive their context unless you stop them
• Silence about limitations is never neutral

Good practice is mostly about writing things down.

What to do next

After this workshop, consider doing just one of the following:

• Add a README to an existing project
• Write a scope and limitations paragraph for a paper
• Fix and record random seeds
• Document a preprocessing step you currently do implicitly
• Add a “not tested on” note to a model description

Choose one thing. Do it once. Let it stick.

How this fits into your career

For PhD students and ECRs especially:

• reproducible work is easier to defend
• clear documentation saves time
• careful scope statements protect you from overclaiming
• FAIR practices increase the lifespan of your work

These benefits show up quickly.

Discussion

Final reflection (optional)

Take one minute and think about:

• One assumption in your current work that could be made explicit
• One future reader you could help with a single sentence

That is enough for today.

Final Thought: Reproducible and ethical ML is not about doing more work. It is about making your work easier to trust and reuse.