Human-Centered AI Frameworks for Socially Responsible Innovation

Abstract

Human-centered AI (HCAI) frameworks place human values, needs, and agency at the centre of AI system design, development, and deployment -- ensuring that AI serves human flourishing rather than treating humans as instruments of AI performance objectives. Socially responsible innovation (SRI) extends this by requiring that AI innovation processes be responsive to societal needs, accountable to affected communities, and reflective about value implications before deployment. This paper proposes the Human-Centered AI for Socially Responsible Innovation Framework (HCAI-SRIF), a systematic evaluation of six HCAI design principles -- human oversight, interpretability and explainability, value alignment, participatory design, reversibility and error correction, and social impact assessment -- across five AI innovation contexts: clinical decision support, autonomous financial advisory, educational AI, AI-assisted creative work, and public policy AI. HCAI-SRIF introduces the Human-Centered Innovation Quality Score (HCIQS) and evaluates 24 AI systems across all five contexts. Key results: clinical decision support achieves the highest HCIQS (0.784) driven by medical governance maturity; autonomous financial advisory achieves the lowest (0.468) due to opacity and human oversight deficit; participatory design is the most underimplemented principle (present in 20.8% of evaluated systems). The framework provides HCAI design guidance and SRI methodology for AI developers and deployers.