Ethical Fairness without Demographics in Human-Centered AI

ArXi:2603.13373v1 Announce Type: cross Computational models are increasingly embedded in human-centered domains such as healthcare, education, workplace analytics, and digital well-being, where their predictions directly influence individual outcomes and collective welfare. In such contexts, achieving high accuracy alone is insufficient; models must also act ethically and equitably across diverse populations. However, fair AI approaches that rely on graphic attributes are impractical, as such information is often unavailable, privacy-sensitive, or restricted by regulatory frameworks.