Any AI system that affects people, rights or critical decisions requires a structured
and transparent risk assessment. This is not a purely technical exercise — it touches
fairness, human rights, accountability and organisational governance.
What Risks Are Assessed?
- Discrimination & fairness risks
- Transparency & explainability gaps
- Data quality & bias in datasets
- Model behaviour & reliability
- Human oversight & escalation paths
- Legal, ethical and societal impact
Types of AI-Related Risk Assessments
- DPIA — data protection and privacy impact assessment
- AI Risk Management (AI Act required) — risk controls for high-risk systems
- Fundamental Rights Assessment — societal and equality impacts
- Model & Data Risk Assessment — behaviour, performance and limitations
How to Conduct One Effectively
- Start with the system’s purpose and context of use
- Bring together legal, technical, ethical and policy expertise
- Identify risks, assumptions and mitigation measures
- Document decisions in a way that supports accountability
- Make risk assessment part of continuous governance, not a one-off task
Why It Matters
Without proper risk assessment, organisations risk unfair outcomes, legal issues,
reputational damage or reduced public trust.
A solid assessment strengthens oversight, improves explainability and ensures
responsible, future-proof AI deployment.