Artificial Intelligence Impact Assessment Guide

Summary

FairNow's AI Impact Assessment Guide is a hands-on template that walks organizations through the complete lifecycle evaluation of AI systems. Unlike academic frameworks that stay at 30,000 feet, this guide gets into the weeds with practical worksheets, decision trees, and checklists that teams can actually use. It bridges the gap between "we should assess our AI" and "here's exactly how to do it," covering everything from initial concept validation to post-deployment monitoring.

What makes this different

Most AI assessment frameworks read like theoretical dissertations. This template flips that script by providing a structured, fill-in-the-blanks approach that busy teams can implement immediately. The guide breaks down complex impact assessments into digestible chunks, with specific prompts for different types of AI applications - from chatbots to recommendation engines to automated decision systems.

The template's strength lies in its systematic progression through risk identification, stakeholder mapping, and mitigation planning. Rather than generic "consider the risks" advice, it provides specific risk categories, probability assessment scales, and impact severity matrices that teams can customize for their context.

Getting started with systematic AI evaluation

The template follows a logical flow that mirrors how AI projects actually develop:

• Pre-deployment phase covers use case definition, stakeholder identification, and preliminary risk screening. The worksheets help teams identify potential blind spots before they become expensive problems.
• Development assessment digs into data quality, model performance metrics, and bias testing protocols. This section includes practical guidance on what metrics to track and when to raise red flags.
• Deployment readiness evaluates monitoring systems, feedback loops, and incident response procedures. The template emphasizes building accountability mechanisms from day one rather than retrofitting them later.
• Ongoing evaluation provides frameworks for regular health checks, performance drift detection, and stakeholder feedback integration.

Who this resource is for

• AI product managers, compliance teams, and technical leads who need to document and justify their AI systems but don't have dedicated governance specialists on staff. It's particularly valuable for mid-size organizations that are beyond the "move fast and break things" startup phase but aren't large enough for dedicated AI ethics teams.
• Legal and compliance professionals will find the structured documentation approach helpful for demonstrating due diligence to regulators and auditors. The template creates a paper trail that shows systematic consideration of risks and impacts.
• Technical teams can use the assessment prompts to identify edge cases and failure modes they might not have considered. The guide's focus on measurable outcomes helps translate business requirements into technical specifications.

Watch out for

This is a template, not a magic wand. The quality of your assessment depends entirely on the honesty and thoroughness of your inputs. Teams that rush through the worksheets or treat them as checkbox exercises will miss the point entirely.

The guide also assumes a certain level of AI literacy. If your team is still figuring out the difference between training and inference, you'll need to pair this with more foundational education resources.

Finally, remember that impact assessment is an ongoing process, not a one-time exercise. The template provides the structure, but you'll need to build the organizational habits to use it consistently throughout your AI system's lifecycle.