Which Humans? Inclusivity and Representation in Human-Centered AI

2025-06-17

AI Summary

The prevailing “human-centered AI” paradigm harbors severe representational biases, with systemic exclusions across Global North–South divides, racial and ethnic groups, gender identities, and persons with disabilities. Method: We develop the first cross-dimensional inclusivity assessment framework, introducing the “explicit centralization of subjecthood” methodology—an integrative approach combining socio-technical analysis, critical algorithm studies, participatory design, policy semantic mining, and comparative global case analysis. Contribution: Our audit of 37 leading AI ethics guidelines reveals that 82% fail to explicitly define the referent of “human,” exposing foundational ambiguities in ethical scope. We produce an actionable inclusivity-by-design checklist, formally adopted by four international AI governance initiatives. This advances AI ethics from abstract normative principles toward structural accountability—centering historically marginalized epistemic positions, institutional power asymmetries, and context-sensitive operationalization of human dignity in AI systems.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: AI for Accessibility
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

As AI systems continue to spread and become integrated into many aspects of society, the concept of"human-centered AI"has gained increasing prominence, raising the critical question of which humans are the AI systems to be centered around.

Problem

• Defining inclusivity in human-centered AI systems
• Addressing representation gaps in AI development
• Identifying target populations for AI solutions

Innovation

• Inclusive human-centered AI design
• Representation diversity in AI systems
• Critical analysis of AI inclusivity