What’s Covered?
The AIIA is both a planning tool and a compliance safeguard. It helps public-sector teams and developers walk through the entire lifecycle of an AI system—purpose, impact, design, data, risks, and accountability—while aligning with the latest EU AI Act requirements.
It’s divided into two main parts:
Part A: Assessment
This section helps teams evaluate the “why” behind the AI system. It includes:
- System purpose and necessity
- Role in the organization and long-term maintainability
- Social and environmental impact
- Whether AI is the right solution at all
Part B: Implementation and Use
Once a project moves forward, this section supports design and deployment. It covers:
- Technical robustness: bias, accuracy, reliability, reproducibility, explainability
- Data governance: quality, integrity, privacy
- Risk management: cybersecurity, fallback procedures
- Accountability: user transparency, verifiability, archiving
The AIIA includes a risk-level classification aligned with the EU AI Act’s categories (minimal, limited, high, unacceptable), and introduces a checklist specifically for generative AI systems. Appendix 1 helps determine whether the system qualifies as “high-risk” under the law, which triggers stricter obligations.
The guide also recommends completing the AIIA with a multidisciplinary team (legal, data, ethics, IT, domain experts), and includes a clear responsibility map for each section. Mandatory (“blue”) and optional (“green”) questions are marked, and all answers must go beyond “yes/no”—forcing thoughtful engagement.
Some key applications:
- Early-stage planning: quick scans to assess feasibility and go/no-go decisions
- Project development: refining design choices based on risk/impact
- Pre-production: mandatory full AIIA completion
- Audits and oversight: provides transparency and documentation trail
This version reflects updates based on user feedback and final EU AI Act provisions, merging more closely with the Netherlands’ Fundamental Rights and Algorithm Impact Assessment (IAMA), reducing the need to fill both tools separately.
💡 Why it matters?
Public institutions are under growing pressure to both innovate with AI and protect rights. The AIIA sets a bar for responsible-by-design systems. It’s not a bureaucratic checkbox—it’s a practical guide to aligning AI with fundamental values, data protection, and legal duties. It’s especially relevant as AI Act enforcement tightens across the EU. Even private companies can learn from this template for internal AI governance.
What’s Missing?
While comprehensive, the AIIA doesn’t include a scoring or risk-weighting mechanism. Teams must qualitatively judge trade-offs without standardized thresholds. There’s also no embedded tracking system for version control or audits over time. And though there’s guidance on team roles, there’s little support for smaller orgs with limited capacity. The framework would also benefit from more detail on accountability in adaptive AI systems that evolve post-deployment, including third-party retraining or plugin models.
There’s also limited support for vendor oversight or external procurement—critical in public-private collaborations. A more robust integration with procurement frameworks or red-teaming practices would make this tool more operationally complete.
Best For:
Government agencies (national or local), public-sector data teams, AI project leads, legal or ethics officers, internal auditors, and developers building AI systems in critical or sensitive areas. It’s also a model worth adapting for enterprise governance programs seeking AI Act alignment.
Source Details:
Title: AI Impact Assessment – Version 2.0
Published by: Ministry of Infrastructure and Water Management (Min I&W), Netherlands
Release Date: December 2024
Collaborators: ILT IDlab, RWS Datalab, CDIB (CIO Office)
Contact: teamai@minienw.nl
Purpose: To provide a structured, mandatory assessment process for AI systems in the public sector that aligns with the EU AI Act, strengthens accountability, and reduces unintended harms. Version 2.0 adds sections on generative AI and reflects new legal thresholds.
Methodology: Based on real deployment experiences, legislation review, and internal audits. It connects with prior Dutch algorithm accountability efforts (e.g. the IAMA), and is tailored to systems with learning components—not just deterministic algorithms.
