| Inter-Pillar Relationships |
| Pillar: Ethical Safeguards |
|
Accountability vs. Fairness |
■ Reinforcing
|
Accountability requires fairness to ensure equitable AI outcomes, linking the two drivers for ethical AI system design
(Ferrara, 2024 )
|
In financial AI systems, fairness audits enhance accountability by preventing discriminatory lending practices and ensuring equitable treatment
(Saura & Debasa, 2022 )
|
|
Accountability vs. Inclusiveness |
■ Reinforcing
|
Accountability ensures inclusiveness by requiring equitable representation and consideration of diverse perspectives in AI system governance
(Leslie, 2019 )
|
A company implements inclusive decision-making practices, reinforcing accountability by reducing disparities in AI outcomes
(Bullock et al., 2024 )
|
|
Accountability vs. Bias Mitigation |
■ Reinforcing
|
Accountability involves ensuring AI systems do not cause harm through biases, closely aligning with bias mitigation efforts to ensure fairness
(Ferrara, 2024 )
|
Regular bias audits reinforce accountability in AI hiring systems, reducing discriminatory outcomes and enhancing fairness
(Cheong, 2024 )
|
|
Accountability vs. Privacy |
■ Tensioned
|
Accountability can conflict with privacy, as complete transparency might infringe on data protection norms
(Solove, 2025 )
|
Implementing exhaustive audit trails ensures accountability but could compromise individuals’ privacy in sensitive sectors
(Solove, 2025 )
|
| Cross-Pillar Relationships |
| Pillar: Ethical Safeguards vs. Operational Integrity |
|
Accountability vs. Governance |
■ Reinforcing
|
Governance frameworks incorporate accountability to ensure decisions in AI systems are monitored, traceable, and responsibly managed
(Bullock et al., 2024 )
|
An AI firm employs governance logs to ensure accountable decision-making, facilitating regulatory compliance and stakeholder trust
(Dubber et al., 2020 )
|
|
Accountability vs. Robustness |
■ Tensioned
|
Accountability’s demand for traceability may compromise robustness by exposing sensitive vulnerabilities
(Leslie, 2019 )
|
In autonomous vehicles, robust privacy measures can conflict with traceability required for accountability
(Busuioc, 2021 )
|
|
Accountability vs. Interpretability |
■ Reinforcing
|
Accountability and interpretability enhance transparency and trust, essential for effective AI system governance
(Dubber et al., 2020 )
|
In finance, regulators use interpretable AI to ensure banks’ accountability by tracking decisions
(Ananny & Crawford, 2018 )
|
|
Accountability vs. Explainability |
■ Reinforcing
|
Accountability promotes explainability by requiring justifications for AI decisions, fostering transparency and informed oversight
(Busuioc, 2021 )
|
Implementing clear explanations in credit scoring ensures accountability and compliance with regulations, enhancing stakeholder trust
(Cheong, 2024 )
|
|
Accountability vs. Security |
■ Reinforcing
|
Accountability enhances security by ensuring responsible data management and risk identification
(Voeneky et al., 2022 )
|
Regular audits on AI systems’ security protocols ensure accountability and safety for data governance
(Voeneky et al., 2022 )
|
|
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 )
|
| Pillar: Ethical Safeguards vs. Societal Empowerment |
|
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 )
|
|
Accountability vs. Human Oversight |
■ Reinforcing
|
Accountability necessitates human oversight for ensuring responsible AI operations, requiring active human involvement and supervision
(Leslie, 2019 )
|
AI systems in healthcare employ human oversight for accountable decision-making, preventing potential adverse outcomes
(Novelli et al., 2024 )
|
|
Accountability vs. Transparency |
■ Reinforcing
|
Transparency supports accountability by enabling oversight and verification of AI systems’ behavior
(Dubber et al., 2020 )
|
In algorithmic finance, transparency enables detailed audits for accountability, curbing unethical financial practices
(Dubber et al., 2020 )
|
|
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 )
|
RAISEF
