| Inter-Pillar Relationships |
| Pillar: Societal Empowerment |
|
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 )
|
|
Human Oversight vs. Trustworthiness |
■ Reinforcing
|
Human oversight enhances AI trustworthiness by ensuring ethical adherence and aligning AI actions with human values
(Dubber et al., 2020 )
|
Continuous human monitoring in secure systems ensures AI actions align with trust standards, boosting user confidence
(Lu et al., 2024 )
|
|
Transparency vs. Trustworthiness |
■ Reinforcing
|
Transparency enhances trustworthiness by clarifying AI operations, fostering informed user relationships
(Floridi et al., 2018 )
|
Transparent AI applications provide clear justifications for decisions, leading to higher user trust
(Floridi et al., 2018 )
|
| Cross-Pillar Relationships |
| Pillar: Ethical Safeguards vs. Societal Empowerment |
|
Fairness vs. Trustworthiness |
■ Reinforcing
|
Fairness enhances trustworthiness by promoting equal treatment, diminishing bias, thus fostering confidence in AI systems
(Cheong, 2024 )
|
Mortgage AI with fair credit evaluations strengthens trustworthiness, ensuring non-discriminatory decisions for applicants
(Dubber et al., 2020 )
|
|
Inclusiveness vs. Trustworthiness |
■ Tensioned
|
Trust-building measures, like rigorous security checks, can marginalize less-privileged stakeholders
(Bullock et al., 2024 )
|
Expensive trust audits in AI systems may exclude smaller organizations from participation
(Dubber et al., 2020 )
|
|
Bias Mitigation vs. Trustworthiness |
■ Reinforcing
|
Bias mitigation fosters trustworthiness by addressing discrimination, thereby improving user confidence in AI systems
(Ferrara, 2024 )
|
In lending AI, bias audits enhance algorithm reliability, fostering trust among users and stakeholders
(Ferrara, 2024 )
|
|
Accountability vs. Trustworthiness |
■ Reinforcing
|
Accountability builds trustworthiness by enhancing transparency and integrity in AI operations
(Schmidpeter & Altenburger, 2023 )
|
AI systems with clear accountability domains are generally more trusted in healthcare settings
(Busuioc, 2021 )
|
|
Privacy vs. Trustworthiness |
■ Reinforcing
|
Privacy measures bolster trustworthiness by safeguarding data against misuse, fostering user confidence
(Lu et al., 2024 )
|
Adopting privacy-centric AI practices enhances trust by ensuring user data isn’t exploited deceptively
(Lu et al., 2024 )
|
| Pillar: Operational Integrity vs. Societal Empowerment |
|
Governance vs. Trustworthiness |
■ Reinforcing
|
Governance frameworks bolster trustworthiness by implementing mechanisms ensuring AI systems adhere to ethical principles
(Gillis et al., 2024 )
|
Trustworthiness in AI is strengthened by governance-mandated transparency and accountability standards
(Bullock et al., 2024 )
|
|
Robustness vs. Trustworthiness |
■ Reinforcing
|
Robustness directly contributes to the trustworthiness of AI by enhancing operational reliability under diverse conditions
(Braiek & Khomh, 2024 )
|
AI models with robust architectures improve trust by reliably handling environmental changes without function loss
(Braiek & Khomh, 2024 )
|
|
Interpretability vs. Trustworthiness |
■ Reinforcing
|
Interpretability boosts trustworthiness by enhancing users’ understanding, encouraging confidence in AI systems
(Rudin, 2019 )
|
Understanding AI predictions in healthcare improves trust in medical diagnostics
(Rudin, 2019 )
|
|
Explainability vs. Trustworthiness |
■ Reinforcing
|
Explainability enhances trustworthiness by providing clarity on AI decisions, reinforcing confidence in system operations
(Toreini et al., 2019 )
|
In financial AI, clear loan decision explanations increase consumer trust in automated evaluations
(Lipton, 2016 )
|
|
Security vs. Trustworthiness |
■ Reinforcing
|
Security underpins trustworthiness by safeguarding AI from breaches, thus enhancing reliability
(Lu et al., 2024 )
|
Secure AI systems, protected against data breaches, inherently build user trust
(Lu et al., 2024 )
|
|
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
