3.2 Impact Assessment Methodology

Comprehensive frameworks for evaluating AI system impacts across algorithmic, privacy, and human rights dimensions with actionable templates and integration strategies.

Key Takeaways

  • Impact assessments are mandatory for high-risk AI under EU AI Act Article 9
  • Three assessment types should be integrated: Algorithmic (AIA), Privacy (DPIA), and Human Rights (HRIA)
  • Assessments must be conducted before deployment and updated throughout the system lifecycle
  • Documentation requirements extend to 10+ years post-system retirement under some regulations

Assessment Framework Overview

Impact assessments form the analytical backbone of responsible AI governance. They transform abstract ethical principles into concrete, documented evaluations that guide development decisions and satisfy regulatory requirements. A robust impact assessment methodology integrates three complementary frameworks:

Assessment Type Primary Focus Legal Basis When Required
Algorithmic Impact Assessment (AIA) Fairness, bias, accuracy, decision-making impacts EU AI Act, Canada AIDA, NYC Local Law 144 All high-risk AI systems
Data Protection Impact Assessment (DPIA) Personal data processing, privacy risks GDPR Article 35, CCPA, state privacy laws High-risk data processing operations
Human Rights Impact Assessment (HRIA) Fundamental rights, societal impacts, vulnerable groups EU AI Act Article 27, UN Guiding Principles High-risk AI, public sector deployments

Integration Imperative

Leading organizations are moving toward integrated impact assessments that combine AIA, DPIA, and HRIA elements into a single, comprehensive evaluation. This approach reduces duplication, ensures consistency, and provides a holistic view of system impacts.

3.2.1 Algorithmic Impact Assessment (AIA) Template

The Algorithmic Impact Assessment evaluates how an AI system's decision-making processes may affect individuals and groups, with particular attention to fairness, accuracy, and accountability. The AIA framework presented here synthesizes requirements from the EU AI Act, Canada's proposed AIDA, and established industry practices.

AIA Structure and Components

Part A: System Description and Context

A.1
System Identification
  • System name, version, and unique identifier
  • Development team and responsible business unit
  • Deployment date and geographic scope
  • Integration points with existing systems
A.2
Purpose and Functionality
  • Primary business objective and use case
  • Decision types (recommendation, classification, prediction, automation)
  • Output format and action triggers
  • Intended beneficiaries and affected populations
A.3
Technical Architecture
  • Model type (ML algorithm, neural network, rule-based, hybrid)
  • Training methodology and data sources
  • Key features and input variables
  • Third-party components and dependencies

Part B: Data Assessment

B.1
Training Data Analysis
  • Data sources and collection methods
  • Dataset size and temporal coverage
  • Demographic representation analysis
  • Known limitations and gaps
B.2
Bias Evaluation
  • Historical bias assessment (does data reflect past discrimination?)
  • Representation bias (are all groups adequately represented?)
  • Measurement bias (are proxies used that correlate with protected characteristics?)
  • Sampling bias (systematic exclusion of populations?)
B.3
Data Quality Metrics
  • Completeness scores by feature
  • Accuracy validation results
  • Freshness and temporal relevance
  • Labeling quality and inter-rater reliability

Part C: Fairness Analysis

Demographic Parity

Proportion of positive outcomes should be equal across groups

P(Ŷ=1|A=0) = P(Ŷ=1|A=1)
Equalized Odds

True positive and false positive rates equal across groups

P(Ŷ=1|Y=y,A=0) = P(Ŷ=1|Y=y,A=1)
Predictive Parity

Precision should be equal across protected groups

P(Y=1|Ŷ=1,A=0) = P(Y=1|Ŷ=1,A=1)
Individual Fairness

Similar individuals should receive similar predictions

d(f(x),f(x')) ≤ L·d(x,x')

Fairness Metric Trade-offs

It is mathematically impossible to satisfy all fairness metrics simultaneously except in trivial cases. Organizations must make explicit choices about which fairness criteria to prioritize based on the specific context and potential harms. Document the rationale for chosen metrics and acknowledge trade-offs.

Part D: Impact Evaluation

Impact Dimension Assessment Questions Risk Level
Economic Impact Does the system affect employment, credit, insurance, or financial opportunities? High
Access to Services Does the system determine access to essential services (healthcare, housing, education)? High
Physical Safety Could system errors result in physical harm? Critical
Psychological Impact Could the system cause emotional distress or psychological harm? Medium
Reputational Impact Could system outputs damage individual or group reputation? Medium
Liberty and Autonomy Does the system affect freedom of movement, expression, or choice? High

Part E: Mitigation Measures

E.1
Technical Mitigations

Document technical measures implemented to address identified risks:

  • Pre-processing: Data augmentation, resampling, feature selection
  • In-processing: Fairness constraints, adversarial debiasing
  • Post-processing: Threshold adjustment, calibration
E.2
Procedural Mitigations

Organizational controls to manage residual risk:

  • Human oversight requirements and escalation paths
  • Appeal and contestation mechanisms
  • Monitoring and audit schedules
E.3
Residual Risk Acceptance

For risks that cannot be fully mitigated:

  • Explicit risk acceptance with business justification
  • Executive sign-off at appropriate level
  • Ongoing monitoring commitment

3.2.2 Data Protection Impact Assessment (DPIA) Integration

Under GDPR Article 35, a DPIA is mandatory when processing is "likely to result in a high risk to the rights and freedoms of natural persons." AI systems frequently trigger this requirement due to their reliance on personal data and automated decision-making capabilities.

DPIA Triggers for AI Systems

🎯

Profiling with Effects

AI that profiles individuals leading to decisions with legal or similarly significant effects

📊

Large-Scale Processing

Processing personal data at scale, particularly special categories (health, biometrics, race)

🔗

Data Combination

Combining datasets in ways data subjects wouldn't reasonably expect

👁️

Monitoring

Systematic monitoring of public spaces or employee behavior

DPIA-AIA Integration Framework

Rather than conducting separate assessments, organizations should integrate DPIA requirements into the broader AIA process. The following mapping shows how DPIA elements align with AIA components:

DPIA Requirement (GDPR Art. 35) AIA Integration Point Documentation Location
Description of processing operations Part A: System Description Section A.1-A.3
Assessment of necessity and proportionality Part A: Purpose and Functionality Section A.2
Assessment of risks to rights and freedoms Part D: Impact Evaluation Section D.1-D.6
Measures to address risks Part E: Mitigation Measures Section E.1-E.3
Data subject consultation (where applicable) Part F: Stakeholder Engagement Supplementary Section

Privacy-Specific AI Considerations

1
Lawful Basis for AI Processing

Identify and document the lawful basis for each processing activity within the AI system. Legitimate interest assessments are required when relying on this basis.

2
Purpose Limitation

Ensure training data was collected for compatible purposes. Document any purpose evolution and assess compatibility under GDPR Article 6(4).

3
Data Minimization

Assess whether all features are necessary. Document feature selection rationale and privacy-enhancing techniques employed.

4
Automated Decision-Making Rights

For systems making decisions with legal or significant effects, document how GDPR Article 22 rights are satisfied (human intervention, right to explanation, right to contest).

5
Special Category Data

If processing reveals or infers special category data (race, health, political opinions, etc.), document the Article 9 exemption relied upon.

Inference Risk

AI systems can infer sensitive information from seemingly innocuous data. A system may not directly process health data but could infer health conditions from purchasing patterns. These inferences may constitute special category data processing, triggering additional GDPR obligations.

3.2.3 Human Rights Impact Assessment

The Human Rights Impact Assessment extends beyond privacy to evaluate AI system impacts on the full spectrum of fundamental rights. This assessment is particularly important for public sector deployments and systems affecting vulnerable populations.

Fundamental Rights Framework

The HRIA should evaluate impacts across internationally recognized human rights instruments:

Right Category Relevant Rights AI Impact Examples
Civil and Political
  • Right to non-discrimination
  • Freedom of expression
  • Right to privacy
  • Right to fair trial
  • Freedom of assembly
 Content moderation, predictive policing, surveillance systems, risk assessment tools
Economic, Social, Cultural
  • Right to work
  • Right to education
  • Right to health
  • Right to housing
  • Right to social security
 Hiring algorithms, educational assessment, diagnostic AI, tenant screening, benefits eligibility
Group Rights
  • Rights of minorities
  • Indigenous peoples' rights
  • Rights of persons with disabilities
  • Children's rights
 Language models excluding minority languages, accessibility barriers, child safety systems

HRIA Methodology

1
Scope Definition

Define the geographic, temporal, and demographic scope of the assessment. Identify all potentially affected stakeholder groups, with particular attention to vulnerable populations.

2
Rights Mapping

Systematically map the AI system's functions against the fundamental rights framework. Identify which rights could potentially be affected by system operations.

3
Stakeholder Consultation

Engage affected communities and civil society organizations. Document consultation methods, participants, and findings. EU AI Act Article 27 requires consultation for fundamental rights assessments.

4
Impact Analysis

Assess the severity, likelihood, and reversibility of potential human rights impacts. Consider both direct impacts and indirect effects through downstream decisions.

5
Mitigation and Remediation

Develop measures to prevent, mitigate, and remediate identified impacts. Establish grievance mechanisms for affected individuals to seek remedy.

Vulnerable Group Analysis

AI systems often disproportionately affect vulnerable populations. The HRIA must include specific analysis of impacts on:

Children and Youth

  • Age verification systems
  • Educational AI impacts
  • Content exposure risks
  • Data protection (special protections)

Persons with Disabilities

  • Accessibility of AI interfaces
  • Bias in recognition systems
  • Employment algorithm impacts
  • Assistive technology interactions

Racial and Ethnic Minorities

  • Facial recognition accuracy
  • Language model representation
  • Credit and lending bias
  • Predictive policing impacts

Low-Income Populations

  • Benefits eligibility systems
  • Insurance pricing algorithms
  • Access to AI-enhanced services
  • Digital divide considerations

Integrated Assessment Approach

Best practice organizations are adopting integrated impact assessment frameworks that combine AIA, DPIA, and HRIA elements into a unified process. This approach offers several advantages:

Efficiency

Single assessment process reduces duplication and stakeholder fatigue

Consistency

Unified framework ensures consistent evaluation criteria across systems

Completeness

Integrated view prevents gaps between assessment types

Governance Alignment

Single process integrates with existing governance structures

Integrated Assessment Workflow

1

Intake & Triage

  • Initial risk classification
  • Assessment scope determination
  • Stakeholder identification
  • Timeline establishment
→
2

Data Gathering

  • Technical documentation review
  • Data inventory compilation
  • Stakeholder interviews
  • Testing and analysis
→
3

Impact Analysis

  • Algorithmic fairness testing
  • Privacy risk evaluation
  • Human rights mapping
  • Risk quantification
→
4

Mitigation Design

  • Technical controls
  • Procedural safeguards
  • Monitoring requirements
  • Residual risk acceptance
→
5

Review & Approval

  • RAI Council review
  • Executive sign-off
  • Documentation finalization
  • Registry entry

Implementation Guide

Assessment Timing Requirements

Assessment Trigger Timing Responsible Party
New AI system development Before design finalization Product Owner + RAI Lead
Third-party AI procurement Before contract execution Procurement + RAI Lead
Significant model update Before deployment to production Model Owner
New use case for existing system Before expanded deployment Business Unit + RAI Lead
Periodic review Annually for high-risk systems Model Owner + Internal Audit
Incident trigger Within 30 days of significant incident RAI Council

Documentation Retention

Retention Requirements

  • EU AI Act: Technical documentation must be retained for 10 years after the AI system is placed on market or put into service
  • GDPR: DPIAs must be retained for the duration of processing and for demonstrating compliance
  • Best Practice: Retain all assessment documentation for system lifetime plus 10 years

Assessment Quality Criteria

Ensure impact assessments meet the following quality standards:

✓
Completeness

All sections addressed with sufficient detail; no gaps or placeholder content

✓
Accuracy

Technical descriptions verified by development team; metrics validated

✓
Independence

Assessment conducted or reviewed by parties independent of development team

✓
Stakeholder Input

Evidence of meaningful consultation with affected parties

✓
Actionability

Clear mitigation measures with owners, timelines, and success criteria