📄 Clause 8.4 – AI System Impact Assessment

Measuring the Real-World Effects of AI

Clause 8.4 goes a step beyond risk and treatment — it’s about understanding the broader consequences of your AI systems, not only on your organization but also on stakeholders, society, and the environment.

An AI System Impact Assessment (AISIA) examines potential and actual impacts from ethical, operational, legal, and social perspectives. It’s a proactive safeguard to ensure AI is deployed responsibly.

✅ What Does Clause 8.4 Require?

Organizations must:

Identify potential impacts — both positive and negative — of AI system deployment.
Assess the likelihood and severity of these impacts across different stakeholder groups.
Evaluate compliance with laws, regulations, ethical commitments, and organizational policies.
Engage stakeholders in the assessment process to get diverse perspectives.
Document and retain evidence of the assessment, decisions, and mitigation actions.
Review and update the assessment when significant changes are made to the AI system or its context.

🧠 Why Is This Critical in AI Governance?

AI can create far-reaching consequences — sometimes unforeseen — that extend beyond direct users:

A hiring AI might inadvertently disadvantage certain groups.
A predictive policing AI could lead to over-surveillance in certain communities.
An environmental AI model could influence large-scale energy use decisions.

Impact assessments help organizations ask:

Who might be harmed or unfairly disadvantaged by this AI system?
Could this AI system create reputational, ethical, or societal issues?
How can positive impacts be maximized while minimizing harm?

🛠️ Implementation Strategy for AI Impact Assessment

Step	Actions
Define scope	Identify AI systems, use cases, and affected stakeholder groups.
Map potential impacts	Cover ethical, operational, legal, environmental, and societal impacts.
Engage stakeholders	Include end users, regulators, ethicists, and impacted communities.
Rate impacts	Use severity-likelihood scoring to prioritize areas for action.
Integrate findings	Feed assessment results into your risk treatment and operational plans.

📝 Examples of AI Impact Assessment Areas

Impact Category	Example
Ethical	AI recruitment tool shows bias against certain demographics.
Operational	AI forecast errors lead to supply chain disruptions.
Legal	AI use violates data privacy laws in certain jurisdictions.
Social	AI-driven recommendations spread misinformation.
Environmental	AI data processing causes significant energy consumption.

🔍 Pro Tip

An AI Impact Assessment is not a one-time compliance task. It should be iterative, tied to model lifecycle stages, and transparent to stakeholders. Publishing high-level summaries of impact assessments can also boost trust and credibility with regulators and the public.

In tomorrow’s article by Kimova.AI, we’ll explore Clause-9 Performance evaluation where we’ll discuss how to continually check whether your AI systems remain effective, compliant, and aligned with your organizational goals.

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