超越公平报酬：NLP众包的伦理影响
Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing
AI总结：
本文指出NLP众包研究在任务伦理考量上的缺失，分析了现有伦理框架Final Rule的不足，建议依据Belmont Report的三项原则评估众包工人面临的风险，并澄清了IRB申请的误解。
摘要：
随着机器学习和AI研究成果呈指数级增长，NLP研究中对众包工人的使用也在迅速增加。NLP研究界内部关于使用众包工人的伦理讨论，通常局限于公平报酬等与劳动条件相关的问题。我们提请人们关注，在与工人执行的各种任务（包括标注、评估和生产）相关的伦理考量方面存在缺失。我们发现，研究人员常用的伦理框架Final Rule并未预见到使用在线众包平台收集数据的情况，导致NLP研究中人类受试者伦理的精神与实践之间出现脱节。我们列举了执行NLP任务的众包工人面临伤害风险的常见场景。因此，我们建议研究人员通过考虑Belmont Report制定的三项伦理原则来评估这些风险。我们还澄清了关于机构审查委员会（IRB）申请的一些常见误解。我们希望本文能促使社区重新展开关于众包工人伦理使用的讨论。
英文摘要：
The use of crowdworkers in NLP research is growing rapidly, in tandem with the exponential increase in research production in machine learning and AI. Ethical discussion regarding the use of crowdworkers within the NLP research community is typically confined in scope to issues related to labor conditions such as fair pay. We draw attention to the lack of ethical considerations related to the various tasks performed by workers, including labeling, evaluation, and production. We find that the Final Rule, the common ethical framework used by researchers, did not anticipate the use of online crowdsourcing platforms for data collection, resulting in gaps between the spirit and practice of human-subjects ethics in NLP research. We enumerate common scenarios where crowdworkers performing NLP tasks are at risk of harm. We thus recommend that researchers evaluate these risks by considering the three ethical principles set up by the Belmont Report. We also clarify some common misconceptions regarding the Institutional Review Board (IRB) application. We hope this paper will serve to reopen the discussion within our community regarding the ethical use of crowdworkers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
