Rate the six risk areas
Score data privacy, security review, license/attribution risk, bias checks, human oversight, and team understanding of AI-generated logic.
AI-ASSISTED DEVELOPMENT
Responsible AI Coding Checklist
Score how well your AI-assisted coding workflow covers privacy, security, attribution, bias, oversight, and human control before you ship.
- Uses your inputs in a transparent calculation
- Instant result with practical next steps
- No signup required
Planning guidance only. Validate important decisions with customer evidence and your delivery team.
YOUR INPUTS
Build your result
Complete every field. The result updates only when you choose Calculate.
How it works
Get a weighted coverage score
Each area is weighted toward the ones most likely to cause real harm if skipped — unreviewed security issues and missing human oversight count most.
Close the highest-risk gaps first
The result ranks concrete next steps so you fix the weakest control before your next AI-assisted merge.
Continue learning: Remote debugging for distributed MVP teams · Transfer third-party tools during MVP handover
Frequently asked questions
Why does human oversight carry so much weight?
AI-generated code that no one on the team reviewed or can explain is the single biggest source of downstream incidents — bugs, security holes, and maintenance debt. A team that always reviews before merge scores far better than one that ships unreviewed AI output.
What counts as an attribution or license risk?
AI models can reproduce patterns from training data with unclear licensing. 'Checked' means you've confirmed generated code doesn't closely reproduce copyrighted or incompatible-license source; 'not checked' means you're shipping without that verification.
How do I check generated code for bias?
Look for assumptions baked into defaults, naming, validation rules, or example data — such as name formats, address fields, or eligibility logic that silently excludes some users. A quick manual read-through against a few varied test cases catches most issues.
Is this checklist legally binding or a compliance certification?
No. It's a self-assessment to surface gaps in your workflow, not a legal, security, or compliance audit. Regulated industries should also involve legal and security specialists.
Does a high score mean the code is bug-free?
No. The score reflects process discipline around AI-assisted coding risk, not code correctness. Keep your normal testing and QA process regardless of the score.
How We Compare
| Feature | MVPHub | GitHub Copilot | Cursor |
|---|---|---|---|
| Standalone risk self-assessment | Included | Not included | Not included |
| Transparent, weighted calculation | Included | Not included | Not included |
| In-editor code suggestions | Not included | Included | Included |
| No repository or account access required | Included | Not included | Not included |
GitHub Copilot and Cursor generate code inside your editor but don't score your team's privacy, security, attribution, bias, and oversight practices around that generated code. MVPHub focuses on the workflow discipline layer, not code generation itself.
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