AI System Impact Assessment Procedure Template| ISO 42001 AIMS

Through AI system impact assessments, the organization discovers risks alongside benefits that help maintain core principle compliance by conducting structured assessments of impacts affecting people and society.

Purpose of AI System Impact Assessment Procedure

The method of assessing AI systems before and during deployment constitutes AI impact assessments. The main goal of this procedure is to meet both our fundamental AI principles and regulatory regulations which will be explained in detail in the sections about AI principles below.

Transparency and Explainability

• The system enables observers to track the steps taken by AI during decision making. Users can easily grasp decision paths through this understanding.
  
  
• The framework helps identify what technical groups and end users need to receive concerning information.
  
  
• The procedure aids in identifying sections of the algorithm that reveal meaning to relevant parties alongside determining areas where additional compensatory controls are necessary.
  
  
• The documentation should specify AI system capabilities alongside its intended purposes for end users to understand AI system decision making.
  
  
• The organization requires assistance for developing communication approaches which guarantee relevant and meaningful explanations for different stakeholders.

Repeatability and Reproducibility

• The organizational documentation standards should follow established guidelines which enable outside parties to reproduce results independently.
  
  
• The organization needs to establish version control systems for all models and data and program code which helps preserve the credibility of system results.
  
  
• The team will contribute to developing procedures that validate systems operate effectively.
  
  
• The field of AI needs standardized scientific standards for making its systems reproducible.
  
  
• Possible physical, psychological and social risks must be identified through their analysis so that protective measures can be established.
  
  
• The assessment enables detection of how fairly benefits from AI systems will spread across different social groups.
  
  
• The system evaluation will demonstrate its effects on social groups together with workplaces and additional settlement communities.
  
  
• The designer will support the implementation of counter measures to minimize environmental impacts by optimizing energy consumption and carbon emissions along with resource efficiency for sustainability purposes.

Security, Privacy and Robustness

• The identification of security and privacy risks in AI systems forms a part of the tasks along with the development of appropriate data protection measures.
  
  
• Assessment shows if AI systems maintain operational resilience against security risks and operational failures that include abnormal input and adversary attacks and component breakdowns.
  
  
• Our organization needs to develop monitoring and security-update structures which span across all AI systems.

Fairness

• These techniques assist organizations in finding possible biases hidden within their collected data and logical algorithms. 
  
  
• The assessment determines if AI systems create negative effects that imbalance across different demographic groups.
  
  
• The system will provide our organization with freedom to select fairness metrics as guided by use context and stakeholder requirements.
  
  
• The organization can use these capabilities to create measures for fighting identified biases during development.

Data Governance

• The data assessment determines if it satisfies necessary standards for maintaining fair and responsible AI performance.
  
  
• The data processing trail must be trackable while we determine the sequence of changes affecting the information.
  
  
• The information should show proof of both consent authorization and adherence to current privacy guidelines.
  
  
• The organization must collect and process data to only the point it is needed.
  
  
• The healthcare organization needs support to develop protocols which address responsible data collection procedures together with storage protocols and deletion procedures.
  

Accountability and Human Agency & Oversight (Controllability)

• Provide control measures that reflect the assessment results of system risks and their impact.
  
  
• The team members will assist with complaint process design through risk assessment and harm identification activities.
  
  
• The human workforce must retain authority to control the activation of AI systems during critical situations.

AI System Impact Assessment Procedure Steps

1. Document AI system details: 

All AI system development initiation along with scoping requirements need documentation during the ideation/brain-storming phase. The documentation requires a list of essential information that starts with the system name and continues with its purpose then documents the business owner and technical lead and finishes with the initial risk classification. The technical documentation should include information about the system framework together with data processing origins and system interface implementations.

2. Assess the Risk Level of the AI System for Proper Classification

The risk classification matrix from our organization needs application to determine if the AI system falls under unacceptable or high or low risk levels (see Appendix B). AI system evaluation should assess its effect on human rights together with safety measures and decision control over systems while determining usage intensity levels. AI systems that operate for automated loan decision-making belong to the high-risk category. The use of an AI system within internal company operations qualifies as a low-risk system.

3. Assessment Protocols Should be Established Based on the Determined Risk Levels of Specific Systems

High-risk systems require in-depth evaluation from three perspectives relating to technology and business aspects along with regulatory standards before their implementation. Longer assessments apply only to systems classified as low risk. The assessment process requires specific determination of what components from data to algorithms and interfaces and decision processes between them will be checked at what analytical depth.

4. Plan the assessment

Each AI system impact assessment needs designated roles to be responsible for conduct and review duties. All assessment activities need proper RACI assignment identification followed by implementation of detailed deliverable and deadline timelines.

5. The Assessment Needs to Have its Required Resources Properly Evaluated.

The assessment process requires both staff members and contractors together with testing tools along with their associated resources.

The assessment process requires the utilization of right decision channels for making requests and gaining necessary resource approvals.

System Purpose and Context Analysis

The System Purpose and Context Analysis stage follows the commencement of AI system impact assessment procedure. The phase requires implementation of the activities described below.

1. The description includes how the AI system works while outlining its main purpose together with exact performance targets and achievement indicators. The AI system examines service interactions for recognizing common issues which leads to suggesting process improvement solutions that aim to shorten resolution times by 20% while enhancing customer satisfaction by 15%.

2. Document each stakeholder group that will work with the AI platform or face effects from its use because functions and interaction levels together with benefits and AI risks need proper documentation. This documentation requires involvement from both direct AI user employees who use the system and indirect stakeholders who have their data processed so direct users can understand AI functionality.

3. The required computing specifications and relevant governmental rules have to be included in the documentation process. The AI systems operate under current privacy regulatory requirements with a mandatory operational stability reaching 99.9% efficacy throughout business hours. Establish all operational conditions that may limit system performance.

Data Assessment

The analysis of data systems through the impact assessment process will proceed into the next step. Multiple steps will be performed to meet this objective.

1. The AI system uses documents to write down and build an inventory of its data sources. Each data resource in the AI system needs to appear in the inventory. The inventory for this system includes internal databases alongside third-party data providers and public datasets combined with the data users generate through the system. The documentation requires all necessary information related to each data source including data types and formats along with automatic update schedules and ownership details and access controls and third-party contract restrictions. 

2. A comprehensive documentation and creation of inventory must address all data sources that feed into the AI system. The inventory process should include all data sources which AI systems use. The inventory process must include evaluation of internal databases alongside third-party data suppliers and public data resources and user-submitted information. Each data source needs documentation which includes data types along with formats and automatic data update intervals and data ownership information and access controls and limits set by third-party contracts. 

3. The AI system's main data sources require an evaluation involving determination of missing value rates and duplication rates alongside statistical assessment of demographic patterns for under-represented populations. 

4. All sensitive data in use by the AI system requires assessment to verify proper compliance with privacy rules. All legitimate bases for processing individual pieces of data must receive written documentation. A data protection impact assessment should be executed as part of the assessment process for high-risk systems. All data movements need to be checked against the requirements dictated by local authorities. An evaluation needs to be conducted for existing privacy-enhancing methods such as anonymization and pseudonymization to assess their performance level.

The management review will include:

a) The status of actions from previous management reviews

b) The management review should analyze existing changes regarding external and internal elements relevant to the AI management system.

c) The review evaluates alterations in the needs and expectations of relevant interested parties regarding the AI management system.

Information about performance outcomes of the AI management system needs evaluation including observational trends from these categories:

1) Nonconformities and corrective actions

2) Monitoring and measurement results

3) Audit results

4) Opportunities for continual improvement.

The results from the management review will decide both the necessary improvements and modifications to the AI management system.

Approval and Decision-Making

AI Ethics and Governance Committee receives AI impact assessment findings through a presentation: Results of the assessment will be formatted and delivered to the AI Ethics and Governance Committee. The staff members from IT and business units will present findings about AI System purpose and impact together with risk protocol and implementation timeline to management.

Conclusion

Organizations gain systematic control through the AI System Impact Assessment Procedure to examine both positive and negative impacts and societal effects of AI system deployments. The assessment represents a vital procedure for both detecting unforeseen consequences and confirming fairness and ethical alignment of AI projects and their legal requirements according to ISO 42001