ABSTRACT

Machine learning (ML) classification models are becoming increasingly popular for tackling the sheer volume and speed of online misinformation. In building these models data scientists need to make assumptions about the legitimacy and authoritativeness of the sources of ‘truth’ employed for model training and testing. This has political, ethical and epistemic implications which are rarely addressed in technical papers. Despite (and due to) their reported high performance, ML-driven moderation systems have the potential to shape public debate and create downstream negative impacts. This article presents findings from a responsible innovation (RI) inflected collaboration between science and technology studies scholars and data scientists. Following an interactive co-ethnographic process, we identify a series of algorithmic contingencies—key moments during ML model development which could lead to different future outcomes, uncertainties and harmful effects. We conclude by offering recommendations on how to address the potential failures of ML tools for combating online misinformation.

摘要

机器学习 (ML) 分类模型在解决在线虚假信息的数量和速度方面变得越来越流行。在构建这些模型时，数据科学家需要对用于模型训练和测试的“真相”来源的合法性和权威性做出假设。这具有政治、伦理和认知方面的影响，而技术论文中很少涉及这些影响。尽管（并且由于）其报告的高性能，机器学习驱动的审核系统有可能影响公众辩论并产生下游负面影响。本文介绍了负责任的创新 (RI) 影响科学技术研究学者和数据科学家之间合作的发现。在互动的共同民族志过程之后，我们确定了一系列算法偶发事件——机器学习模型开发过程中的关键时刻，这些时刻可能会导致不同的未来结果、不确定性和有害影响。最后，我们提供了有关如何解决机器学习工具在打击在线错误信息方面的潜在失败的建议。