Within the open model ecosystem, AI model marketplaces enable non-AI experts to create and distribute derivative AI models. These platforms establish RAI approaches that creators are expected to follow to prevent negative downstream effects from model deployment, yet such governance approaches often rely on creators' intrinsic motivation and integrity. Little is known about how individual creators conceptualize their accountability for derivative models and how these conceptualizations shape their RAI practices throughout the development cycle. Through semi-structured interviews with 19 creators of derivative image-generation models on open model marketplaces, we identify two distinct accountability logics underlying creators' development practices: commons-based peer production and personal creative property. Guided by these logics, creators engage with platform RAI approaches in ways that diverge from their intended purposes: they strategically repurpose attribution tools for model visibility rather than provenance transparency, displace platform authority in favor of community-based governance, restrict model access to manage downstream accountability costs, and treat RAI-related work as undervalued labor to be minimized. We discuss how self-regulatory use of RAI tools, shaped by individuals' perceptions, produces shortcomings, and how open model marketplaces might mitigate these limitations by structurally supporting creators' engagement with RAI practices.
Shuheng Chen, Namratha Patil, Haonan Pan, Angel Hsing-Chi Hwang University of Southern California, Yao Du University of Southern California, Ruishan Liu University of Southern California, Jieyu Zhao University of Southern California
Clinical decision-making requires synthesizing heterogeneous evidence, including patient histories, clinical guidelines, and trajectories of comparable cases. While large language models (LLMs) offer strong reasoning capabilities, they remain prone to hallucinations and struggle to integrate long, structured medical documents. We present MED-COPILOT, an interactive research prototype for evidence-aware clinical reasoning, designed to help clinicians and medical trainees inspect guideline-level and patient-level evidence. MED-COPILOT combines guideline-grounded GraphRAG retrieval with hybrid semantic-keyword similar-patient retrieval to support transparent and evidence-aware clinical reasoning. The system builds a structured knowledge graph from WHO and NICE guidelines, applies community-level summarization for efficient retrieval, and maintains a 36,000-case similar-patient database derived from SOAP-normalized MIMIC-IV notes and Synthea-generated records.We evaluate our framework on clinical note completion and medical question answering, and demonstrate that it consistently outperforms parametric LLM baselines and standard RAG, improving generation fidelity and benchmark QA accuracy. The full system is available at https://huggingface.co/spaces/shuhengc/MED-COPILOT, enabling users to inspect retrieved evidence, visualize token-level similarity contributions, and conduct guided follow-up analysis. Our results suggest a practical and interpretable approach to integrating structured guideline knowledge with patient-level analogical evidence for clinical LLMs.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表