语音演员在AI数据经济中面临隐私与声誉风险，亟需新治理框架。
PRAC3 (Privacy, Reputation, Accountability, Consent, Credit, Compensation): Long Tailed Risks of Voice Actors in AI Data-Economy
- 提出PRAC3框架，扩展C3伦理模型以涵盖隐私、声誉与问责。
- 20位配音演员访谈显示，声音被滥用致色情内容、诈骗等风险。
- 适合关注AI伦理、数字劳工权益及声音数据治理的研究者。
早期大规模音频数据集（如LibriSpeech）依赖数百名配音演员的贡献，推动了语音技术发展。十年后，这些声音却使演员暴露于新型风险中。现有伦理框架虽强调同意、署名与补偿（C3），但未能应对声音身份脱离上下文、作者权与控制力的挑战。通过对20位专业配音演员的质性访谈发现，未经约束的声音合成复制导致多重威胁：声音被用于色情内容、政治攻击或网络迷因，甚至在金融诈骗、虚假信息传播等高风险场景中被克隆使用。在此类事件中，演员面临社会与法律后果却无救济途径，且多数缺乏法律代表或工会保护。为此，本文提出PRAC3框架，将隐私、声誉、问责、同意、署名与补偿作为合成语音经济中的相互关联支柱。该框架揭示非授权训练如何放大隐私风险，去语境化部署如何引发声誉损害，并重新思考人工智能数据生态中的问责机制。文章主张，作为生物特征标识与创造性劳动，声音需要恢复创作者自主权、确保可追溯性并建立可执行的伦理再利用边界。
原文摘要 · Abstract (English)
Early large-scale audio datasets, such as LibriSpeech, were built with hundreds of individual contributors whose voices were instrumental in the development of speech technologies, including audiobooks and voice assistants. Yet, a decade later, these same contributions have exposed voice actors to a range of risks. While existing ethical frameworks emphasize Consent, Credit, and Compensation (C3), they do not adequately address the emergent risks involving vocal identities that are increasingly decoupled from context, authorship, and control. Drawing on qualitative interviews with 20 professional voice actors, this paper reveals how the synthetic replication of voice without enforceable constraints exposes individuals to a range of threats. Beyond reputational harm, such as re-purposing voice data in erotic content, offensive political messaging, and meme culture, we document concerns about accountability breakdowns when their voice is leveraged to clone voices that are deployed in high-stakes scenarios such as financial fraud, misinformation campaigns, or impersonation scams. In such cases, actors face social and legal fallout without recourse, while very few of them have a legal representative or union protection. To make sense of these shifting dynamics, we introduce the PRAC3 framework, an expansion of C3 that foregrounds Privacy, Reputation, Accountability, Consent, Credit, and Compensation as interdependent pillars of data used in the synthetic voice economy. This framework captures how privacy risks are amplified through non-consensual training, how reputational harm arises from decontextualized deployment, and how accountability can be reimagined AI Data ecosystems. We argue that voice, as both a biometric identifier and creative labor, demands governance models that restore creator agency, ensure traceability, and establish enforceable boundaries for ethical reuse.
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