| Inter-Pillar Relationships |
| Pillar: Operational Integrity |
|
Governance vs. Safety |
■ Reinforcing
|
Governance frameworks enhance safety by establishing standards ensuring AI operational integrity and harm prevention
(Bullock et al., 2024 )
|
AI governance mandates safety protocols in autonomous vehicles to prevent malfunctions and accidents
(Fjeld et al., 2020 )
|
|
Robustness vs. Safety |
■ Reinforcing
|
Robustness ensures AI operates safely under challenging conditions, enhancing overall safety
(Leslie, 2019 )
|
In medicine, robust AI systems reliably identify anomalies despite data distribution changes, promoting safety
(Leslie, 2019 )
|
|
Interpretability vs. Safety |
■ Reinforcing
|
Interpretability aids safety by enhancing understandability and identifying system flaws
(Leslie, 2019 )
|
In medical AI, interpretable models allow doctors to verify predictions, improving safety
(Leslie, 2019 )
|
|
Explainability vs. Safety |
■ Reinforcing
|
Explainability enhances safety by making AI decision processes transparent and aiding risk assessment
(Dubber et al., 2020 )
|
Explainable models in autonomous vehicles help identify decision-making flaws, promoting operational safety
(Dubber et al., 2020 )
|
|
Safety vs. Security |
■ Tensioned
|
Adversarial robustness efforts enhance security but may reduce safety by increasing complexity
(Braiek & Khomh, 2024 )
|
Autonomous vehicle safety protocols might focus on preventing adversarial attacks at the expense of real-world robustness
(Leslie, 2019 )
|
| Cross-Pillar Relationships |
| Pillar: Ethical Safeguards vs. Operational Integrity |
|
Fairness vs. Safety |
■ Tensioned
|
Fairness can conflict with safety since safety may require restrictive measures that impact equitable access
(Leslie, 2019 )
|
Self-driving algorithms balanced between passenger safety and fair pedestrian detection can lead to safety and fairness trade-offs
(Cath, 2018 )
|
|
Inclusiveness vs. Safety |
■ Reinforcing
|
Inclusiveness motivates safety by ensuring diverse needs are considered, enhancing overall safety standards
(Fosch-Villaronga & Poulsen, 2022 )
|
Inclusive AI health tools accommodate diverse groups, improving overall safety and personalized healthcare
(World Health Organization, 2021 )
|
|
Bias Mitigation vs. Safety |
■ Reinforcing
|
Bias mitigation increases safety by addressing discrimination risks, central to safe AI deployment
(Ferrara, 2024 )
|
Ensuring fair training data mitigates bias-related risks in AI models, enhancing safety in autonomous vehicles
(Ferrara, 2024 )
|
|
Accountability vs. Safety |
■ Reinforcing
|
Accountability ensures AI safety by demanding human oversight and system verification, enhancing procedural safeguards
(Leslie, 2019 )
|
In autonomous vehicles, accountability through rigorous standards enhances safety measures ensuring fail-safe operations
(Leslie, 2019 )
|
|
Privacy vs. Safety |
■ Tensioned
|
Balancing privacy protection with ensuring safety can cause ethical dilemmas in AI systems
(Bullock et al., 2024 )
|
AI in autonomous vehicles must handle data privacy while addressing safety features
(Bullock et al., 2024 )
|
| Pillar: Operational Integrity vs. Societal Empowerment |
|
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 )
|
|
Human Oversight vs. Safety |
■ Reinforcing
|
Human oversight improves safety by providing necessary monitoring and intervention capabilities in AI operations
(Bullock et al., 2024 )
|
In aviation, human oversight actively ensures safety by intervening during unexpected autonomous system failures
(Williams & Yampolskiy, 2024 )
|
|
Safety vs. Transparency |
■ Reinforcing
|
Transparency reinforces safety by enabling detection and mitigation of risks effectively
(Leslie, 2019 )
|
Clear documentation of AI processes ensures safety, enabling effective oversight and risk management
(Leslie, 2019 )
|
|
Safety vs. Trustworthiness |
■ Reinforcing
|
Safety measures enhance AI systems’ trustworthiness by ensuring reliability and robust risk management
(Leslie, 2019 )
|
Safety protocols in autonomous vehicles improve trustworthiness, ensuring public confidence and acceptance of the technology
(Leslie, 2019 )
|
RAISEF
