超越偏见：文化人工智能的参与式与反思式方法
Beyond Bias: Participatory and Reflective Approaches to Cultural AI
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中文总结 AI 辅助
该研究通过Beyond Bias项目，以参与式方法开展文化AI实践，经9场200余人参与的工作坊，开发相关工具与框架，揭示生成式AI可视化对文化表征的调节作用，为文化AI领域提供新路径。
中文摘要 AI 辅助
生成式人工智能系统正日益塑造文化创作，但创意意图、文化意义与阐释实践往往无法仅通过计算指标清晰表达。本文介绍Beyond Bias项目，该项目由该机构（此处原文为this http URL，保留原名）与印度歌德学院合作开展，是一种文化人工智能的参与式方法，涵盖协作式数据集创建、反思式AI工具开发、艺术家主导的模型微调以及共同制定的治理实践。在9场共有200余名参与者的工作坊中，艺术家与文化从业者通过实验、迭代及协作式LoRA训练与AI系统互动。参与者将AI生成的输出与可视化内容用作反思界面，探索象征意义、记忆、作者身份及文化语境。将当代生成式AI输出与参与者微调后的输出进行对比，帮助参与者反思大型科技公司AI系统中缺失的文化细节。本文贡献包括：以透明度、管理与社区参与为核心的反思式AI工具方法；参与式工作坊的研究发现，探讨生成式AI可视化如何调节文化表征与阐释实践；以及基于文化完整性与反思实践的文化人工智能框架。
英文摘要
Generative AI systems increasingly shape cultural production, yet creative intentions, cultural meanings, and interpretive practices often can't be articulated through computational metrics alone. This paper presents Beyond Bias, a collaboration between Gooey.AI and Goethe-Institut India, as a participatory approach to cultural AI which includes collaborative dataset creation, reflective AI tooling, artist-led model fine-tuning, and co-authored governance practices. Across 9 workshops involving over 200 participants, artists and cultural practitioners engaged with AI systems through experimentation, iteration, and collaborative LoRA training. Participants used their AI-generated outputs and visualizations as reflective interfaces for exploring symbolism, memory, authorship, and cultural contexts. Comparing contemporary generative AI outputs with participant fine-tuned outputs helped participants reflect on cultural details missing in big tech AI systems. This paper contributes reflective AI tooling approaches foregrounding transparency, stewardship, and community participation; findings from participatory workshops examining how generative AI visualizations mediate cultural representation and interpretive practice; and a framework for cultural AI grounded in cultural integrity, and reflective practice.
发表机构
- Royal College of Art（皇家艺术学院）
- Goethe-Institut India（歌德学院印度）
机构由 AI 辅助整理，请以论文原文为准。
