A.8 Sustainability Management

4.1 Analyse the environmental, social, and economic performance of AI systems in applied project settings, such as optimising machine-learning workflows for energy efficiency.

Assessed by Sustainability analysis report of an AI system, assessed for analysis of environmental, social, and economic performance in an applied project setting.

4.2 Evaluate alternative AI implementation strategies for their sustainability impact by comparing model, data, or infrastructure choices, and propose feasible improvements.

Assessed by Comparative evaluation matrix with written improvement proposal, assessed for comparison of model, data, or infrastructure choices and for feasibility of proposed sustainability improvements.

4.3 Apply sustainability assessment tools and methods to AI projects using established frameworks or metrics, under minimal supervision.

Assessed by Completed sustainability assessment framework applied to an AI project, assessed for correct use of established tools, frameworks, or metrics under minimal supervision.

4.4 Report findings and recommendations to team members and stakeholders in professional project documentation or presentations, for improving AI practices.

Assessed by Project presentation or written project memo, assessed for clear reporting of findings and actionable recommendations to team members and stakeholders for improving AI practices.

4.5 Identify common sustainability risks and trade-offs in AI systems in practical development or deployment scenarios, such as data volume, model size, or hardware use.

Assessed by Sustainability risk and trade-off identification document, assessed for correct identification of common AI sustainability risks and trade-offs related to data volume, model size, or hardware use.

4.6 Reflect on personal and team responsibilities in applying sustainable AI practices within supervised professional or educational projects.

Assessed by Structured reflective statement on sustainable AI practice, assessed for reflection on personal and team responsibilities within supervised professional or educational projects.

1.1 Analyse the environmental, social, and economic performance of AI systems in applied project settings, such as optimising machine-learning workflows for energy efficiency.

Assessed by Sustainability analysis report of an AI system, assessed for analysis of environmental, social, and economic performance in an applied project setting, including energy-efficiency considerations where relevant.

1.2 Evaluate alternative AI implementation strategies for their sustainability impact by comparing model, data, or infrastructure choices, and propose feasible improvements.

Assessed by Comparative evaluation matrix with written improvement proposal, assessed for comparison of model, data, or infrastructure choices and for feasibility of proposed sustainability improvements.

1.3 Apply sustainability assessment tools and methods to AI projects using established frameworks or metrics, under minimal supervision.

Assessed by Completed sustainability assessment framework applied to an AI project, assessed for correct use of established sustainability tools, frameworks, or metrics under minimal supervision.

1.4 Report findings and recommendations to team members and stakeholders in professional project documentation or presentations, for improving AI practices.

Assessed by Project presentation or written project memo, assessed for clear reporting of findings and actionable recommendations to team members and stakeholders for improving AI practices.

1.5 Identify common sustainability risks and trade-offs in AI systems in practical development or deployment scenarios, such as data volume, model size, or hardware use.

Assessed by Sustainability risk and trade-off identification document, assessed for correct identification of common AI sustainability risks and trade-offs related to data volume, model size, or hardware use in practical development or deployment scenarios.

1.6 Reflect on personal and team responsibilities in applying sustainable AI practices within supervised professional or educational projects.

Assessed by Structured reflective statement on sustainable AI practice, assessed for reflection on personal and team responsibilities in applying sustainable AI practices within supervised professional or educational projects.

2.1 Analyse the environmental, social, and economic performance of AI systems in applied project settings, such as optimising machine-learning workflows for energy efficiency.

Assessed by Sustainability analysis report of an AI system assessed for analysis of environmental, social, and economic performance in an applied project setting, including energy-efficiency considerations where relevant.

2.2 Evaluate alternative AI implementation strategies for their sustainability impact by comparing model, data, or infrastructure choices, and propose feasible improvements.

Assessed by Comparative evaluation matrix with written improvement proposal assessing sustainability impacts of alternative model, data, or infrastructure choices and feasibility of proposed improvements.

2.3 Apply sustainability assessment tools and methods to AI projects using established frameworks or metrics, under minimal supervision.

Assessed by Completed sustainability assessment framework applied to an AI project, assessed for correct use of established sustainability tools, methods, frameworks, or metrics under minimal supervision.

2.4 Report findings and recommendations to team members and stakeholders in professional project documentation or presentations, for improving AI practices.

Assessed by Project presentation or written project memo assessed for clear reporting of findings and actionable recommendations to team members and stakeholders for improving AI practices.

2.5 Identify common sustainability risks and trade-offs in AI systems in practical development or deployment scenarios, such as data volume, model size, or hardware use.

Assessed by Sustainability risk and trade-off identification document assessed for identification of practical AI sustainability risks and trade-offs, including data volume, model size, or hardware use where applicable.

2.6 Reflect on personal and team responsibilities in applying sustainable AI practices within supervised professional or educational projects.

Assessed by Structured reflective statement on sustainable AI practice assessed for reflection on personal and team responsibilities within supervised professional or educational projects.

2.1 Analyse the environmental, social, and economic performance of AI systems in applied project settings, such as optimising machine-learning workflows for energy efficiency.

Assessed by Sustainability analysis report of an AI system assessed for analysis of environmental, social, and economic performance in an applied project setting, including energy-efficiency considerations where relevant.

2.2 Evaluate alternative AI implementation strategies for their sustainability impact by comparing model, data, or infrastructure choices, and propose feasible improvements.

Assessed by Comparative evaluation matrix with written improvement proposal assessing sustainability impacts of alternative model, data, or infrastructure choices and feasibility of proposed improvements.

2.3 Apply sustainability assessment tools and methods to AI projects using established frameworks or metrics, under minimal supervision.

Assessed by Completed sustainability assessment framework applied to an AI project, assessed for correct use of established sustainability tools, methods, frameworks, or metrics under minimal supervision.

2.4 Report findings and recommendations to team members and stakeholders in professional project documentation or presentations, for improving AI practices.

Assessed by Project presentation or written project memo assessed for clear reporting of findings and actionable recommendations to team members and stakeholders for improving AI practices.

2.5 Identify common sustainability risks and trade-offs in AI systems in practical development or deployment scenarios, such as data volume, model size, or hardware use.

Assessed by Sustainability risk and trade-off identification document assessed for identification of practical AI sustainability risks and trade-offs, including data volume, model size, or hardware use where applicable.

2.6 Reflect on personal and team responsibilities in applying sustainable AI practices within supervised professional or educational projects.

Assessed by Structured reflective statement on sustainable AI practice assessed for reflection on personal and team responsibilities within supervised professional or educational projects.

3.1 Design AI deployment strategies that optimise environmental, social, and economic outcomes across organisational projects, such as portfolio-level AI planning.

Assessed by Sustainable AI strategy document assessed for design of AI deployment strategies that optimize environmental, social, and economic outcomes across organizational projects.

3.2 Coordinate cross-functional teams to implement sustainable AI practices in professional or organisational environments, including technical and non-technical roles.

Assessed by Cross-functional coordination plan with role and process definition, assessed for coordination of technical and non-technical roles to implement sustainable AI practices.

3.3 Assess the long-term impacts of AI initiatives on organisational sustainability objectives by considering lifecycle, scaling, and governance effects.

Assessed by Long-term AI sustainability impact assessment report assessed for lifecycle, scaling, and governance effects on organizational sustainability objectives.

3.4 Advise stakeholders on responsible AI practices in strategic decision-making contexts, including energy-efficient and socially equitable AI solutions.

Assessed by Responsible AI advisory brief for stakeholders, assessed for advice on responsible AI practices, energy-efficient solutions, and socially equitable AI solutions in strategic decision- making contexts.

3.5 Compare and prioritise sustainability interventions for AI systems using organisational constraints and strategic goals, to support informed decision-making.

Assessed by Sustainability intervention prioritization framework assessed for comparison and prioritization of AI sustainability interventions using organizational constraints and strategic goals.

3.6 Translate organisational sustainability policies into AI-related requirements within project or program planning processes.

Assessed by Policy-to-AI requirement mapping document assessed for accurate translation of organizational sustainability policies into AI-related project or program requirements.

5.1 Design AI deployment strategies that optimise environmental, social, and economic outcomes across organisational projects, such as portfolio-level AI planning.

Assessed by Sustainable AI strategy document assessed for design of AI deployment strategies that optimize environmental, social, and economic outcomes across organizational projects.

5.2 Coordinate cross-functional teams to implement sustainable AI practices in professional or organisational environments, including technical and non-technical roles.

Assessed by Cross-functional coordination plan with role and process definition assessed for coordination of technical and non-technical roles to implement sustainable AI practices.

5.3 Assess the long-term impacts of AI initiatives on organisational sustainability objectives by considering lifecycle, scaling, and governance effects.

Assessed by Long-term AI sustainability impact assessment report assessed for lifecycle, scaling, and governance effects on organizational sustainability objectives.

5.4 Advise stakeholders on responsible AI practices in strategic decision-making contexts, including energy-efficient and socially equitable AI solutions.

Assessed by Responsible AI advisory brief for stakeholders assessed for advice on responsible AI practices, energy-efficient solutions, and socially equitable AI solutions in strategic decision- making contexts.

5.5 Compare and prioritise sustainability interventions for AI systems using organisational constraints and strategic goals, to support informed decision-making.

Assessed by Sustainability intervention prioritization framework assessed for comparison and prioritization of AI sustainability interventions using organizational constraints and strategic goals.

5.6 Translate organisational sustainability policies into AI-related requirements within project or program planning processes.

Assessed by Policy-to-AI requirement mapping document assessed for accurate translation of organizational sustainability policies into AI-related project or program requirements.

4.1 Design AI deployment strategies that optimise environmental, social, and economic outcomes across organisational projects, such as portfolio-level AI planning.

Assessed by Sustainable AI strategy document assessed for design of AI deployment strategies that optimize environmental, social, and economic outcomes across organizational projects.

4.2 Coordinate cross-functional teams to implement sustainable AI practices in professional or organisational environments, including technical and non-technical roles.

Assessed by Cross-functional coordination plan with role and process definition assessed for coordination of technical and non-technical roles to implement sustainable AI practices.

4.3 Assess the long-term impacts of AI initiatives on organisational sustainability objectives by considering lifecycle, scaling, and governance effects.

Assessed by Long-term AI sustainability impact assessment report assessed for lifecycle, scaling, and governance effects on organizational sustainability objectives.

4.4 Advise stakeholders on responsible AI practices in strategic decision-making contexts, including energy-efficient and socially equitable AI solutions.

Assessed by Responsible AI advisory brief for stakeholders assessed for advice on responsible AI practices, energy-efficient solutions, and socially equitable AI solutions in strategic decision- making contexts.

4.5 Compare and prioritise sustainability interventions for AI systems using organisational constraints and strategic goals, to support informed decision-making.

Assessed by Sustainability intervention prioritization framework assessed for comparison and prioritization of AI sustainability interventions using organizational constraints and strategic goals.

4.6 Translate organisational sustainability policies into AI-related requirements within project or program planning processes.

Assessed by Policy-to-AI requirement mapping document assessed for accurate translation of organizational sustainability policies into AI-related project or program requirements.