Responsible AI
Responsible practice is not a separate workstream for us — it is how the work is done. These are the commitments we bring to every engagement involving AI and data.
We work with the minimum data needed for the task, handle it carefully, and are clear about where it lives and how it is used. We do not repurpose client data, and we favour approaches that keep sensitive information under your control.
Systems we build are documented in plain language: what they do, how they work, what data they rely on, and how to operate them. Documentation is a deliverable, not an afterthought, so the work remains reviewable and maintainable.
AI behaviour is measured, not assumed. We define evaluation criteria and test sets up front, check changes against them, and report results honestly — including where performance is uneven or uncertain.
AI assists people; it does not replace their judgment on consequential decisions. We design explicit checkpoints where a person reviews, approves, or can override the system, and we make those points visible in the workflow.
Models and data carry assumptions and can reflect bias. We look for it, name it where we find it, and consider who could be affected. Where we cannot fully resolve a concern, we document it so it can be weighed in the decision.
Every system has limits. We are direct about what a tool is not suited for, the conditions under which it may be unreliable, and the assumptions baked into it — so it is used within the bounds it was built for.
These commitments describe how we intend to work, and we hold ourselves to them. They are not a guarantee about any particular outcome or system — responsible AI is an ongoing practice that depends on context, data, and the decisions a team makes. We are happy to discuss how these principles would apply to your specific situation.
Work with Northstar
Tell us about the system or decision you have in mind, and we'll walk through how these principles would shape the work.