| Inter-Pillar Relationships |
| Pillar: Ethical Safeguards |
|
Fairness vs. Inclusiveness |
■ Reinforcing
|
Inclusiveness enhances fairness by broadening AI’s scope to reflect diverse societal elements equitably
(Shams et al., 2023 )
|
Inclusive AI hiring prevents gender disparity by reflecting diversity through fair data representation
(Shams et al., 2023 )
|
|
Bias Mitigation vs. Fairness |
■ Tensioned
|
While both aim to reduce injustices, techniques for fairness (e.g., demographic parity) can sometimes contradict bias mitigation goals
(Ferrara, 2024 )
|
Ensuring demographic parity in hiring algorithms might lead to the over-representation of certain groups, raising concerns about individual fairness
(Dubber et al., 2020 )
|
|
Bias Mitigation vs. Inclusiveness |
■ Reinforcing
|
Bias mitigation supports inclusiveness by actively addressing representation gaps, enhancing fairness across AI applications
(Ferrara, 2024 )
|
Implementing bias audits ensures diverse datasets in educational AI models, promoting inclusiveness
(Ferrara, 2024 )
|
|
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 )
|
|
Fairness vs. Privacy |
■ Tensioned
|
Tensions arise as fairness needs ample data, potentially conflicting with privacy expectations
(Cheong, 2024 )
|
Fair lending AI seeks demographic data for fairness, challenging privacy rights
(Cheong, 2024 )
|
|
Inclusiveness vs. Privacy |
■ Tensioned
|
Privacy-preserving techniques can limit data diversity, compromising inclusiveness
(d’Aliberti et al., 2024 )
|
Differential privacy in healthcare AI might obscure patterns relevant to minority groups
(d’Aliberti et al., 2024 )
|
|
Bias Mitigation vs. Privacy |
■ Tensioned
|
Bias mitigation can conflict with privacy when data diversity requires sensitive personal information
(Ferrara, 2024 )
|
Healthcare AI often struggles to balance privacy laws with the need for diverse training data
(Ferrara, 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 )
|
| Pillar: Operational Integrity |
|
Governance vs. Robustness |
■ Reinforcing
|
Governance frameworks support robustness by establishing guidelines to ensure AI systems are resilient and reliable
(Batool et al., 2023 )
|
AI governance mandates robustness assurance in healthcare to ensure reliable system performance under varying conditions
(Bullock et al., 2024 )
|
|
Governance vs. Interpretability |
■ Reinforcing
|
Governance supports interpretability by enforcing standards to ensure AI systems are understandable and transparent
(Bullock et al., 2024 )
|
AI regulations mandate interpretability to validate algorithmic outputs, ensuring systems comply with governance frameworks
(Bullock et al., 2024 )
|
|
Interpretability vs. Robustness |
■ Tensioned
|
Interpretability can compromise robustness due to increased complexity in models
(Hamon et al., 2020 )
|
Interpretable models in safety-critical applications may reduce robustness, increasing vulnerability to adversarial attacks
(Hamon et al., 2020 )
|
|
Explainability vs. Governance |
■ Reinforcing
|
Explainability enhances governance by providing insights needed for informed oversight and decision-making
(Bullock et al., 2024 )
|
In regulatory contexts, clear AI explanations help policymakers ensure compliance and adapt regulations effectively
(Bullock et al., 2024 )
|
|
Explainability vs. Robustness |
■ Tensioned
|
High explainability may simplify models, potentially reducing their robustness
(Rudin, 2019 )
|
Simplified credit scoring models for explainability may perform poorly under non-standard conditions
(Rudin, 2019 )
|
|
Explainability vs. Interpretability |
■ Reinforcing
|
Explainability aids interpretability by clarifying complex model outputs for user understanding
(Hamon et al., 2020 )
|
In financial AI, explainable models improve decision insight, ensuring model actionability
(Hamon et al., 2020 )
|
|
Governance vs. Security |
■ Reinforcing
|
Governance strengthens security by setting protocols and standards to protect against AI threats
(Bullock et al., 2024 )
|
Governance mandates security audits in AI deployments to ensure adherence to best practices and protocols
(Habbal et al., 2024 )
|
|
Robustness vs. Security |
■ Reinforcing
|
Robustness strengthens security against adversarial attacks, enhancing overall system reliability
(Habbal et al., 2024 )
|
Robust AI enhances security by withstanding data poisoning, crucial in cybersecurity
(Habbal et al., 2024 )
|
|
Interpretability vs. Security |
■ Tensioned
|
Security demands limited openness; interpretability requires transparency, creating inherent conflict
(Bommasani et al., 2021 )
|
Interpretable models in healthcare might expose vulnerabilities if too transparent, affecting security
(Rudin, 2019 )
|
|
Explainability vs. Security |
■ Tensioned
|
Explainability can expose security vulnerabilities by revealing AI system operations
(Hamon et al., 2020 )
|
Detailed explanations of security systems can aid adversaries in identifying exploitable weaknesses
(Hamon et al., 2020 )
|
|
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 )
|
| 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 )
|
|
Human Oversight vs. Transparency |
■ Reinforcing
|
Human oversight and transparency collectively foster accountability, enhancing ethical governance in AI systems
(UNESCO, 2022 )
|
In AI-driven medical diagnostics, both drivers ensure user trust and effective oversight
(Ananny & Crawford, 2018 )
|
|
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. Operational Integrity |
|
Fairness vs. Governance |
■ Reinforcing
|
Governance ensures fairness by establishing regulatory frameworks that guide AI systems towards unbiased practices
(Cath, 2018 )
|
The EU AI Act mandates fairness algorithms under governance to prevent discrimination in employment
(Cath, 2018 )
|
|
Governance vs. Inclusiveness |
■ Reinforcing
|
Governance frameworks establish inclusive participation, ensuring representation of various groups in AI decision-making
(Bullock et al., 2024 )
|
Governance mandates diverse stakeholder involvement to ensure inclusiveness in AI policy development
(Zowghi & Da Rimini, 2024 )
|
|
Bias Mitigation vs. Governance |
■ Reinforcing
|
Governance frameworks regularly incorporate bias mitigation strategies, reinforcing ethical AI implementation
(Ferrara, 2024 )
|
AI governance policies in finance often include bias audits, ensuring ethical compliance
(Ferrara, 2024 )
|
|
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 )
|
|
Governance vs. Privacy |
■ Tensioned
|
Governance mandates can challenge privacy priorities, as regulations may require data access contrary to privacy safeguards
(Mittelstadt, 2019 )
|
Regulatory monitoring demands could infringe on personal privacy by requiring detailed data disclosures for compliance
(Solow-Niederman, 2022 )
|
|
Fairness vs. Robustness |
■ Tensioned
|
Fairness might necessitate modifications that decrease robustness
(Tocchetti et al., 2022 )
|
Adjustments to AI models for fairness in loan approvals might reduce performance across datasets
(Braiek & Khomh, 2024 )
|
|
Inclusiveness vs. Robustness |
■ Tensioned
|
Inclusiveness may compromise robustness by prioritizing accessibility over resilience in challenging scenarios
(Leslie, 2019 )
|
AI built for varied groups might underperform in rigorous environments, highlighting tension
(Leslie, 2019 )
|
|
Bias Mitigation vs. Robustness |
■ Reinforcing
|
Bias mitigation enhances robustness by incorporating diverse data, reducing systematic vulnerabilities in AI models
(Ferrara, 2024 )
|
Inclusive datasets in AI model training improve both bias mitigation and system robustness
(Ferrara, 2024 )
|
|
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 )
|
|
Privacy vs. Robustness |
■ Tensioned
|
Achieving high privacy can sometimes challenge robustness by limiting data availability
(Hamon et al., 2020 )
|
Differential privacy techniques may decrease robustness, impacting AI model performance in varied conditions
(Hamon et al., 2020 )
|
|
Fairness vs. Interpretability |
■ Reinforcing
|
Interpretability fosters fairness by making opaque AI systems comprehensible, allowing equitable scrutiny and accountability
(Binns, 2018 )
|
Interpretable algorithms in credit scoring identify biases, supporting fairness standards and promoting equitable lending
(Bateni et al., 2022 )
|
|
Inclusiveness vs. Interpretability |
■ Reinforcing
|
Interpretability enriches inclusiveness by ensuring AI systems are understandable, fostering wide accessibility and equitable application
(Shams et al., 2023 )
|
Interpretable AI frameworks enable diverse communities’ meaningful engagement by clarifying system decisions, supporting inclusive practices
(Cheong, 2024 )
|
|
Bias Mitigation vs. Interpretability |
■ Reinforcing
|
Interpretability aids bias detection, supporting equitable AI systems by elucidating model decisions
(Ferrara, 2024 )
|
Interpretable healthcare models reveal biases in diagnostic outputs, promoting fair treatment
(Ferrara, 2024 )
|
|
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 )
|
|
Interpretability vs. Privacy |
■ Tensioned
|
Privacy constraints often limit model transparency, complicating interpretability
(Cheong, 2024 )
|
In healthcare, strict privacy laws can impede clear interpretability, affecting decisions on patient data
(Wachter & Mittelstadt, 2019 )
|
|
Explainability vs. Fairness |
■ Reinforcing
|
Explainability assists in ensuring fairness by elucidating biases, enabling equitable AI systems
(Ferrara, 2024 )
|
In credit scoring, explainable models help identify discrimination, promoting fairer lending practices
(Ferrara, 2024 )
|
|
Explainability vs. Inclusiveness |
■ Reinforcing
|
Explainability promotes inclusiveness by making AI decisions understandable, encouraging equitable stakeholder participation
(Shams et al., 2023 )
|
Explainable AI models help identify underrepresented groups’ needs, ensuring inclusive design in public policy
(Shams et al., 2023 )
|
|
Bias Mitigation vs. Explainability |
■ Tensioned
|
Bias mitigation can obscure model operations, conflicting with the transparency needed for explainability
(Rudin, 2019 )
|
In high-stakes justice applications, improving model explainability can compromise bias mitigation efforts
(Busuioc, 2021 )
|
|
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 )
|
|
Explainability vs. Privacy |
■ Tensioned
|
Explainability can jeopardize privacy by revealing sensitive algorithm details
(Solove, 2025 )
|
Disclosing algorithm logic in healthcare AI might infringe patient data privacy
(Solove, 2025 )
|
|
Fairness vs. Security |
■ Tensioned
|
Fairness needs data transparency, often conflicting with strict security protocols prohibiting data access
(Leslie et al., 2024 )
|
Ensuring fair user data access can compromise data security boundaries, posing organizational security risks
(Leslie et al., 2024 )
|
|
Inclusiveness vs. Security |
■ Tensioned
|
Inclusiveness in AI can expose it to vulnerabilities, challenging security measures
(Fosch-Villaronga & Poulsen, 2022 ;
Zowghi & Da Rimini, 2024 )
|
Inclusive AI systems might prioritize accessibility but compromise security, as noted when addressing diverse infrastructures
(Microsoft, 2022 )
|
|
Bias Mitigation vs. Security |
■ Reinforcing
|
Bias mitigation enhances security by reducing vulnerabilities that arise from discriminatory models
(Habbal et al., 2024 )
|
Including bias audits in AI-driven fraud detection systems strengthens security protocols
(Habbal et al., 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 )
|
|
Privacy vs. Security |
■ Reinforcing
|
Both privacy and security strive for safeguarding sensitive data, aligning objectives
(Hu et al., 2021 )
|
Using encryption methods, AI systems ensure privacy while maintaining security, protecting data integrity
(Hu et al., 2021 )
|
|
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: 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 )
|
|
Fairness vs. Human Oversight |
■ Reinforcing
|
Human oversight supports fairness by ensuring AI decisions reflect equitable practices grounded in human judgment
(Voeneky et al., 2022 )
|
For recruitment AI, human oversight calibrates fairness, reviewing bias mitigation strategies before final implementation
(Bateni et al., 2022 )
|
|
Human Oversight vs. Inclusiveness |
■ Reinforcing
|
Human oversight promotes inclusiveness by ensuring diverse perspectives shape AI ethics and implementation
(Dubber et al., 2020 )
|
Human oversight in AI enhances inclusiveness by involving diverse stakeholder consultations during system development
(Zowghi & Da Rimini, 2024 )
|
|
Bias Mitigation vs. Human Oversight |
■ Reinforcing
|
Human oversight supports bias mitigation by ensuring continual auditing to detect and address biases
(Ferrara, 2024 )
|
In hiring AI, human oversight helps identify bias in training data biases, enhancing fairness
(Ferrara, 2024 )
|
|
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 )
|
|
Human Oversight vs. Privacy |
■ Tensioned
|
Human oversight might collide with privacy, requiring access to sensitive data for supervision
(Solove, 2025 )
|
AI deployment often requires human oversight conflicting with privacy norms to evaluate sensitive data algorithms
(Dubber et al., 2020 )
|
|
Fairness vs. Transparency |
■ Reinforcing
|
Transparency in AI increases fairness by allowing for the identification and correction of biases
(Ferrara, 2024 )
|
Transparent hiring algorithms enable fairness by revealing discriminatory patterns in recruitment practices
(Lu et al., 2024 )
|
|
Inclusiveness vs. Transparency |
■ Reinforcing
|
Both inclusiveness and transparency promote equitable access and understanding in AI, enhancing collaborative growth
(Buijsman, 2024 )
|
Diverse teams enhance transparency tools in AI systems, ensuring fair representation and increased public understanding
(Buijsman, 2024 )
|
|
Bias Mitigation vs. Transparency |
■ Reinforcing
|
Bias mitigation relies on transparency to ensure fair AI systems by revealing discriminatory patterns
(Ferrara, 2024 )
|
Transparent algorithms in recruitment help identify bias in decision processes, ensuring fair practices
(Ferrara, 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 )
|
|
Privacy vs. Transparency |
■ Tensioned
|
High transparency can inadvertently compromise user privacy
(Cheong, 2024 )
|
Algorithm registries disclose data sources but risk exposing personal data
(Buijsman, 2024 )
|
|
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. 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 )
|
|
Governance vs. Human Oversight |
■ Reinforcing
|
Governance frameworks guide human oversight, ensuring responsible decision-making, enhancing effective AI system regulation
(Bullock et al., 2024 )
|
Regulations require human oversight for AI use in healthcare, ensuring ethical decisions aligned with governance mandates
(Yeung et al., 2019)
|
|
Human Oversight vs. Robustness |
■ Reinforcing
|
Human oversight strengthens robustness by mitigating risks through active monitoring and intervention
(Tocchetti et al., 2022 )
|
Human oversight ensures robust system behavior during AI deployment in high-stakes environments like aviation
(High-Level Expert Group on Artificial Intelligence, 2020 )
|
|
Human Oversight vs. Interpretability |
■ Reinforcing
|
Human oversight bolsters interpretability by guiding transparency in AI processes, ensuring systems remain clear to users
(Hamon et al., 2020 )
|
Interpretable algorithms in medical AI gain user trust through human-supervised transparency during their development
(Doshi-Velez & Kim, 2017 )
|
|
Explainability vs. Human Oversight |
■ Reinforcing
|
Explainability enhances human oversight by providing clear model outputs, aiding in decision-making accuracy
(UNESCO, 2022 )
|
In healthcare, explainable AI systems allow practitioners to verify treatment recommendations, ensuring oversight
(UNESCO, 2022 )
|
|
Human Oversight vs. Security |
■ Reinforcing
|
Human oversight enhances security by providing checks against unauthorized access and misuse in AI systems
(Lu et al., 2024 )
|
Security protocols are strengthened by human oversight to monitor potential AI system breaches
(Dubber et al., 2020 )
|
|
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 )
|
|
Governance vs. Transparency |
■ Reinforcing
|
Governance frameworks enhance transparency, mandating disclosure and open practices to ensure accountability in AI systems
(Bullock et al., 2024 )
|
Governance laws requiring transparent AI audits bolster accountability, fostering public trust in government-aligned AI use
(Batool et al., 2023 )
|
|
Robustness vs. Transparency |
■ Reinforcing
|
Robustness enhances transparency by providing consistent operations, reducing opaque behaviors
(Hamon et al., 2020 )
|
Greater AI robustness minimizes erratic outcomes, facilitating clearer system transparency
(Hamon et al., 2020 )
|
|
Interpretability vs. Transparency |
■ Reinforcing
|
Interpretability enhances transparency by providing insights into AI mechanisms, fortifying user understanding
(Lipton, 2016 )
|
Transparent models boost public trust, as stakeholders understand how AI decisions are made clearly
(Lipton, 2016 )
|
|
Explainability vs. Transparency |
■ Reinforcing
|
Both explainability and transparency enhance trust by making AI systems’ inner workings and decisions understandability essential for accountability
(Cheong, 2024 )
|
In healthcare AI, both drive accessible patient diagnosis explanations and transparent model algorithms
(Ananny & Crawford, 2018 )
|
|
Security vs. Transparency |
■ Tensioned
|
Security needs might impede transparency efforts, as disclosure could expose vulnerabilities
(Bullock et al., 2024 )
|
When AI transparency compromises security, it can lead to potential breaches, hindering open communications
(Bullock et al., 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 )
|
|
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
