Why Fairness Matters

  • Equitable outcomes: Ensures benefits and burdens are distributed fairly across relevant groups, not just on average performance.
  • Fitness-for-purpose: Validates that the system works for the populations it will actually serve, reducing hidden failure rates in under-represented contexts.
  • Trust and adoption: Makes decisions explainable and defensible to users, reviewers, and impacted communities, which increases uptake and reduces complaints.
  • Regulatory and ethical risk: Lowers exposure to discrimination claims and compliance failures by documenting objectives, tests, and remediation paths.

When Fairness Is Missed

In 2019, a widely used U.S. healthcare risk algorithm allocated extra care based on predicted cost rather than true clinical need. Because Black patients historically incurred lower costs for the same level of illness, the system underestimated their risk and steered resources away from them. Audits revealed the bias and switching to a need-based target improved equity. The lesson: when fairness objectives and subgroup checks are absent, seemingly neutral proxies can encode historic disparities and systematically deny people appropriate services.

Fairness Inter-Driver Relationship List

The following table summarizes the 14 fairness related, inter-driver relationships. The full 105 relationships can be viewed here:

Note: The convention when displaying drivers Ds vs. Dt, is to display the first driver alphabetically as Ds.

Drivers Relationship Explanation Example
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 )
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 )
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 )
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 )
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 )
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 )
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 )
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 )
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 )
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 )
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 )
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 )
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 )

© 2025, Richard R. Khan. All rights reserved. Design: HTML5 UP.
Privacy Policy | Terms of Use | Cookie Policy