Daniel Schiff (Purdue U) has posted “Strategies for Harmonizing Fragmented AI Ethics Frameworks, Standards, and Regulations” (https://doi.org/10.1007/978-981-97-8440-0_82-1) on SSRN. Here is the abstract:
AI governance is increasingly shaped by a patchwork of ethical frameworks, standards, and regulations, with overlapping demands from technical standards bodies, industry consortia, and governments. A key piece of this puzzle is standardization: as regulators increasingly delegate governance to standards development organizations (SDOs) in response to rapid AI innovation, hundreds of potentially overlapping standards proliferate, creating redundancy, organizational confusion, and superficial compliance, while audits and certifications struggle to meaningfully assess adherence. Drawing on an analysis of over 500 AI standards across multiple domains and issuing bodies, this chapter diagnoses five interrelated challenges complicating AI governance: the persistent difficulty of translating contested sociotechnical concepts like fairness, well-being, and transparency into actionable standards; the proliferation of voluntary frameworks with limited enforceability and vague operational guidance; decision paralysis as organizations confront competing and overlapping standards; standards development cycles that lag behind fastmoving AI innovation; and geopolitical competition that fragments international coordination. To address these challenges, the chapter offers a detailed set of institutional design strategies, grounded in current governance practice, for both harmonizing-and humanizing-AI governance. These include fostering deeper coordination among SDOs, supporting satisficing and layered framework adoption tailored to organizational capacity, establishing living standards and rapid-response taskforces to enable adaptive updates, strengthening auditing ecosystems through independent accreditation, transparency, and public reporting, and embedding stakeholder participation directly into governance workflows and organizational implementation processes. Crucially, AI governance must itself be human-centered, designed to be legible, adaptable, and actionable for the real humans responsible for implementing, auditing, and overseeing AI systems.
Please cite: Schiff, D. S. (2025). Strategies for Harmonizing Fragmented AI Ethics Frameworks, Standards, and Regulations. InHandbook of Human-Centered Artificial Intelligence (pp. 1–45). Springer.https://doi.org/10.1007/978-981-97-8440-0_82-1