| Inter-Pillar Relationships |
| Pillar: Societal Empowerment |
|
Human Oversight vs. Sustainability |
■ Reinforcing
|
Human oversight supports sustainable AI, ensuring ethical standards are achieved, reducing environmental impacts
(Dubber et al., 2020 )
|
AI projects evaluated with human oversight consider sustainability impacts, aligning environmental goals with tech innovations
(Rohde et al., 2023 )
|
|
Sustainability vs. Transparency |
■ Neutral
|
Sustainability and transparency influence AI’s lifecycle but don’t directly conflict or reinforce, promoting governance synergy
(van Wynsberghe, 2021 )
|
Deploying sustainable AI while maintaining transparency in energy sourcing exemplifies balanced governance goals in AI systems
(van Wynsberghe, 2021 )
|
|
Sustainability vs. Trustworthiness |
■ Reinforcing
|
Sustainability and trustworthiness together enhance long-term responsible AI deployment, creating societal and environmental benefits
(van Wynsberghe, 2021 )
|
Implementing energy-efficient AI models can increase trust, aligning with corporate sustainability goals
(Accenture, 2024 )
|
| Cross-Pillar Relationships |
| Pillar: Ethical Safeguards vs. Societal Empowerment |
|
Fairness vs. Sustainability |
■ Reinforcing
|
Fairness supports sustainability by advocating equitable resource distribution, essential for sustainable AI solutions
(Schmidpeter & Altenburger, 2023 )
|
AI systems ensuring fair access to renewable energy results underscore this synergy
(van Wynsberghe, 2021 )
|
|
Inclusiveness vs. Sustainability |
■ Reinforcing
|
Inclusive AI development inherently supports sustainable goals by considering diverse needs and reducing inequalities
(van Wynsberghe, 2021 )
|
AI initiatives promoting inclusivity often align with sustainability, as seen in projects that address accessibility in green technologies
(van Wynsberghe, 2021 )
|
|
Bias Mitigation vs. Sustainability |
■ Reinforcing
|
Bias mitigation supports sustainability by fostering fair access to AI benefits, reducing societal imbalances
(Rohde et al., 2023 )
|
Ensuring AI equitable data distribution reduces systemic biases, contributing to sustainable growth
(Rohde et al., 2023 )
|
|
Accountability vs. Sustainability |
■ Reinforcing
|
Accountability aligns with sustainability by ensuring responsible practices that support ecological integrity and social justice
(van Wynsberghe, 2021 )
|
Implementing accountable AI practices reduces carbon footprint while enhancing brand trust through sustainable operations
(van Wynsberghe, 2021 )
|
|
Privacy vs. Sustainability |
■ Tensioned
|
Privacy demands limit data availability, hindering AI’s potential to achieve sustainability goals
(van Wynsberghe, 2021 )
|
Strict privacy laws restrict data collection necessary for AI to optimize urban energy use
(Bullock et al., 2024 )
|
| Pillar: Operational Integrity vs. Societal Empowerment |
|
Governance vs. Sustainability |
■ Reinforcing
|
Governance establishes guidelines supporting sustainable AI practices, ensuring long-term societal and environmental benefits
(Schmidpeter & Altenburger, 2023 )
|
Sustainability standards mandated by governance frameworks ensure energy-efficient AI development and deployment practices
(van Wynsberghe, 2021 )
|
|
Robustness vs. Sustainability |
■ Tensioned
|
Minimizing energy consumption could compromise robustness under variable conditions
(Carayannis & Grigoroudis, 2023 )
|
Energy-efficient machine learning models may struggle with edge-case data
(Braiek & Khomh, 2024 )
|
|
Interpretability vs. Sustainability |
■ Neutral
|
Interpretability and sustainability operate independently, focusing on different AI aspects
(van Wynsberghe, 2021 )
|
An AI model could be interpretable but unsustainable due to high computational demands
(van Wynsberghe, 2021 )
|
|
Explainability vs. Sustainability |
■ Reinforcing
|
Explainability aids sustainable AI practices by ensuring accountable development and deployment, promoting ethical standards
(Schmidpeter & Altenburger, 2023 )
|
AI systems explaining carbon footprints can align sustainability goals with operational transparency
(Hamon et al., 2020 )
|
|
Security vs. Sustainability |
■ Neutral
|
Security and sustainability address different areas, with minimal direct overlap in AI system design
(van Wynsberghe, 2021 )
|
An AI system could be secure without considering sustainability impacts like energy use
(van Wynsberghe, 2021 )
|
|
Safety vs. Sustainability |
■ Reinforcing
|
Safety measures contribute to the responsible lifecycle management, essential for sustainability in AI projects
(van Wynsberghe, 2021 )
|
Applying safety protocols in AI reduces environmental risks, contributing to sustainable management practices
(van Wynsberghe, 2021 )
|
RAISEF
