Impact Assessment
A systematic evaluation of the potential effects an AI system may have on individuals, groups, and society. Impact assessments consider both positive outcomes and potential harms.
Why It Matters
Impact assessments are becoming mandatory for high-risk AI under the EU AI Act. They force organizations to think about consequences before deployment, not after.
Example
Evaluating a hiring AI by considering impacts on different demographic groups, employment patterns, candidate experience, and long-term organizational diversity.
Think of it like...
Like an environmental impact assessment before building a dam — you study all the potential effects, both positive and negative, before proceeding.
Related Terms
AI Governance
The frameworks, policies, processes, and organizational structures that guide the responsible development, deployment, and monitoring of AI systems within organizations and across society.
Risk Assessment
The systematic process of identifying, analyzing, and evaluating potential risks associated with an AI system. Risk assessment considers both the likelihood and impact of potential harms.
Fairness
The principle that AI systems should treat all individuals and groups equitably and not produce discriminatory outcomes. Multiple mathematical definitions of fairness exist, and they can sometimes conflict.
Bias in AI
Systematic errors in AI outputs that unfairly favor or disadvantage certain groups based on characteristics like race, gender, age, or socioeconomic status. Bias can originate from training data, model design, or deployment context.
EU AI Act
The European Union's comprehensive regulatory framework for artificial intelligence, establishing rules based on risk levels. It categorizes AI systems from minimal to unacceptable risk with corresponding compliance requirements.