AI Impact Assessment: Definition and Examples

AI Impact Assessment is a methodical approach to identify, analyze, and mitigate the risks associated with the deployment of an artificial intelligence system. Inspired by environmental impact studies or privacy impact assessments (PIA), this process examines the consequences of an AI system across several dimensions: ethical, social, legal, economic, and technical.

Concretely, an AI impact assessment reviews criteria such as algorithmic biases, transparency of decisions, protection of personal data, fairness in user treatment, and effects on employment or fundamental rights. It typically includes stakeholder mapping, risk scenario analysis, and the definition of corrective measures. This process is increasingly required by regulatory frameworks, notably the European AI Act, which mandates conformity assessments for high-risk AI systems.

In the context of prompt engineering, understanding AI Impact Assessment allows designing more responsible prompts, embedding safeguards against biases, hallucinations, or misuse from the outset. A knowledgeable practitioner will be able to anticipate the impacts of their AI systems and document decisions made to ensure ethical and compliant deployment.

Impact assessment is not a one-time exercise but a continuous process that must be updated with every significant change to the model, training data, or usage context. It constitutes a pillar of responsible AI governance.