> For the complete documentation index, see [llms.txt](https://trust.impact.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://trust.impact.com/responsible-by-design-our-methodology.md).

# Responsible by Design: Our Methodology

## Responsible by Design Methodology

We embed our principles into every stage of the development lifecycle, not just at the end. This is what "Responsible-by-Design" means in practice. Every in-platform AI capability at impact.com moves through these six phases.

{% stepper %}
{% step %}

### Requirements

* **Governance & risk review:** a pre-spec review with senior stakeholders before resources are committed.
* **Technological intentionality:** we ask, "Is AI the right tool for this problem?" and only commit to AI when its benefits demonstrably outweigh the risks.
* **Centralized oversight:** all AI projects are tracked in a single space for full visibility across the company.
  {% endstep %}

{% step %}

### Design

* **AI identification:** clearly label every AI interaction for the user.
* **User control & feedback:** opt-outs, settings, and feedback channels built in.
* **Inclusive & accessible design:** features usable by everyone, including people who rely on assistive technologies.
* **Privacy-by-design architecture:** data minimization and robust authorization built into the foundation.
  {% endstep %}

{% step %}

### Implementation

* **Robust audit trails:** log significant AI actions on a per-user basis for accountability.
* **Secure coding:** protect the system and its data from vulnerabilities.
  {% endstep %}

{% step %}

### Testing

* **Intensive security testing:** vetted internally and by external white-hat experts.
* **Fairness & bias audits:** check for performance disparities and apply safety tooling (such as Google's Model Armor).
  {% endstep %}

{% step %}

### Deployment

* **Transparent documentation:** model cards, help articles, and specs for different audiences.
* **Continuous monitoring:** track performance and drift, with humans in the loop.
  {% endstep %}

{% step %}

### Maintenance & continuous improvement

* **Iterative feedback loops:** feedback from monitoring, real user interactions, and testing flows back into the lifecycle.
* **A learning culture:** an active internal AI community shares knowledge and challenges our assumptions, so we keep improving and stay ahead of emerging risks.
  {% endstep %}
  {% endstepper %}


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