AI Ethics Considerations We Bring to Client Projects

Before building an AI feature that affects hiring, lending, or other consequential decisions, we ask clients what happens when the AI is wrong, and who is accountable for that outcome.

We also check training and reference data for biases that could produce unfair outcomes across different customer groups, since a model is only as fair as the data it learns from.

These conversations happen at the scoping stage, not after launch, since it is far cheaper to adjust a design than to retrofit fairness into a system already in production.