Types of AI Impact Assessments
AI impact assessments evaluate the potential effects of AI systems on individuals, groups, and society. Different assessment types focus on different impact dimensions.
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Human Rights Impact
Effects on fundamental rights and freedoms
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Algorithmic Impact
Bias, fairness, and decision quality
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Social Impact
Effects on communities and society
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Environmental Impact
Energy, resources, and sustainability
Fundamental Rights Impact Assessment (FRIA)
Required under EU AI Act for certain deployers of high-risk AI systems. Assesses impacts on fundamental rights protected under EU Charter.
Rights to Assess
- Human Dignity (Art. 1): Respect for inherent human worth
- Privacy (Art. 7): Respect for private and family life
- Data Protection (Art. 8): Protection of personal data
- Non-Discrimination (Art. 21): Prohibition of discrimination
- Equality (Art. 20, 23): Equal treatment and gender equality
- Fair Trial (Art. 47): Access to justice and effective remedy
- Workers' Rights (Art. 27-31): Employment protections
FRIA Process
- Describe the AI system and its deployment context
- Identify affected groups and stakeholders
- Map potential impacts on each relevant right
- Assess severity and likelihood of impacts
- Identify mitigation measures
- Document findings and maintain records
- Notify market surveillance authority (if required)
Algorithmic Impact Assessment (AIA)
Focuses specifically on the decision-making characteristics of AI systems.
Key Assessment Areas
| Area | Questions to Address |
|---|
| Fairness | Are outcomes equitable across demographic groups? What fairness metrics apply? |
| Accuracy | What is the error rate? How are errors distributed across groups? |
| Transparency | Can decisions be explained? To what level of detail? |
| Accountability | Who is responsible for decisions? What recourse exists? |
| Contestability | Can decisions be challenged? What appeal process exists? |
Environmental Impact Assessment
Evaluates the environmental footprint of AI systems, increasingly important for ESG compliance.
Impact Categories
- Training Energy: Electricity consumed during model training
- Inference Energy: Ongoing operational energy consumption
- Hardware Lifecycle: Manufacturing, use, and disposal of computing equipment
- Data Center Impact: Cooling, water usage, land use
- Carbon Footprint: Total greenhouse gas emissions
💡 Growing Regulatory Focus
EU AI Act requires providers to report energy consumption and environmental impact. Organizations should track AI carbon footprint as part of ESG reporting.
Assessment Integration
Organizations often integrate multiple assessment types into a unified AI Impact Assessment (AIA) process.
Integrated Assessment Framework
- Scoping: Define AI system, use case, and affected stakeholders
- Rights Mapping: Identify relevant human rights implications
- Algorithmic Analysis: Assess fairness, accuracy, and explainability
- Social Analysis: Evaluate community and societal impacts
- Environmental Analysis: Calculate resource consumption and emissions
- Risk Aggregation: Combine findings into overall risk profile
- Mitigation Planning: Develop controls for identified impacts
- Documentation: Create assessment record for governance and compliance
- Review: Periodic reassessment as system and context evolve