# SCOR: A Framework for Responsible AI Innovation in Digital Ecosystems

arXiv: https://arxiv.org/abs/2509.10653 · HTML: https://mlsanity.com/paper/2509.10653/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 12 Sep 2025.

## Builds on (most cited first, 5 of 5)

- [Understanding artificial intelligence ethics and safety](https://mlsanity.com/paper/1906.05684/) (2019)
- [Diversity and Inclusion in Artificial Intelligence](https://mlsanity.com/paper/2305.12728/) (2023)
- [Stronger Together: on the Articulation of Ethical Charters, Legal Tools, and Technical Documentation in ML](https://mlsanity.com/paper/2305.18615/) (2023)
- [Putting AI Ethics into Practice: The Hourglass Model of Organizational AI Governance](https://mlsanity.com/paper/2206.00335/) (2022)
- [Emerging Practices in Participatory AI Design in Public Sector Innovation](https://mlsanity.com/paper/2502.18689/) (2025)

## Abstract (arXiv)

AI-driven digital ecosystems span diverse stakeholders including technology firms, regulators, accelerators and civil society, yet often lack cohesive ethical governance. This paper proposes a four-pillar framework (SCOR) to embed accountability, fairness, and inclusivity across such multi-actor networks. Leveraging a design science approach, we develop a Shared Ethical Charter(S), structured Co-Design and Stakeholder Engagement protocols(C), a system of Continuous Oversight and Learning(O), and Adaptive Regulatory Alignment strategies(R). Each component includes practical guidance, from lite modules for resource-constrained start-ups to in-depth auditing systems for larger consortia. Through illustrative vignettes in healthcare, finance, and smart city contexts, we demonstrate how the framework can harmonize organizational culture, leadership incentives, and cross-jurisdictional compliance. Our mixed-method KPI design further ensures that quantitative targets are complemented by qualitative assessments of user trust and cultural change. By uniting ethical principles with scalable operational structures, this paper offers a replicable pathway toward responsible AI innovation in complex digital ecosystems.