Investigating public discourses around decision-making algorithms using a combined computational and discursive language analysis approach

这个项目探讨了使用跨学科的透镜调查面向公众的决策算法的话语范围。面向公众的决策算法旨在提高生产力，并实现更有效和更明智的决策。例子包括英国的Covid-19数字联系人追踪应用程序和2020年使用的Ofqual A Level自动算法。这些工作没有监督，对联合王国公民产生影响。研究对自治系统的信任对于未来人工智能技术的发展至关重要，因为它们与我们的日常生活越来越相关。值得信赖的自治系统中心采用Devitt的观点，即自治系统必须在设计和感知方面值得信赖。这里收集的意见将作为验证或路标，以确定是否需要进行替代探索。为了分析所表达的意见，可以使用计算语言学方法，包括主题建模、情感分析和情感检测。与访谈或实验相反，这些可以是非侵入性的和具有成本效益的。数据来源可能是社交媒体，特别是Twitter，因为许多受这些算法影响的人在这个公共访问网站上提供意见，提供了Twitter的API可以实时分析的大型数据集。然而，这些方法显示出对社交媒体上讨论决策算法意见的话语和对话方式的认识有限，这应该被解释为更详细地理解这一点。通过初步的工作，流行的计算语言学方法的缺点包括否定，讽刺和讽刺的分类问题和难以解释。一种新的方法可以克服这些缺点，结合计算语言学和社会语言学分析方法，如语料库语言学和话语分析，其中语境起着重要的作用。创建一个混合的方法来分析周围的话语面向公众的决策算法可能会产生更好的质量的见解，打破信任和adaptation.This项目的障碍调查以下问题：-当前形式的计算语言分析如何支持周围的决策算法的社交媒体公共话语的理解？有哪些优点和缺点？如何通过将话语分析和语料库语言学与计算语言学分析结合起来，更详细地理解社交媒体上围绕面向公众的决策算法的话语？现有的计算语言学分析可以通过哪些方式与定性语言学分析相结合，以减轻现有方法的缺点？

This project examines the scope for investigating the discourse surrounding public-facing decision-making algorithms using an interdisciplinary lens. Public-facing decision-making algorithms aim to increase productivity and enable more efficient and informed decision-making. Examples include the UK's Covid-19 digital contact-tracing application and the Ofqual A Level automated algorithm used in 2020. These work without supervision and had impact on United Kingdom citizens. Investigating trust in Autonomous Systems is critical for the development of future artificial intelligence technologies as they become more relevant to our daily lives. The Trustworthy Autonomous Systems Hub adopts Devitt's view that Autonomous Systems must be trustworthy by design and perception.The collection of opinions here will act as validation or signpost the need for alternative exploration. To analyse the views expressed, computational linguistic methods - including topic modelling, sentiment analysis and emotion detection - may be deployed. These can be non-intrusive and cost-effective, as opposed to interviews or experiments. A source of data may be social media, notably Twitter, as many who have been affected by these algorithms offer opinions on this public-access site, providing a large data set that Twitter's API can analyse in real-time. However, these methods show limited awareness of the discursive and conversational ways in which opinions on decision-making algorithms are discussed on social media, which should be accounted for to understand this in further detail. Through initial work, popular computational linguistic methods' shortcomings include issues with the classification of negation, sarcasm and irony and difficulty in interpretation. A new approach may overcome these shortcomings by combining computational linguistic and sociolinguistic analytical methods, such as corpus linguistics and discourse analysis, where context plays an important role. Creating a hybrid approach to analysing the discourse surrounding public-facing decision-making algorithms may produce better quality insights to break down barriers to trust and adoption.This project investigates the following questions:- How can current forms of computational linguistic analysis support the understanding of social media public discourses around decision-making algorithms? What are the benefits and shortcomings?- How can the discourse surrounding public-facing decision-making algorithms on social media be understood in more detail through the inclusion of discourse analysis and corpus linguistics alongside computational linguistic analysis?- In what ways can existing computational linguistic analysis be combined with qualitative linguistic analysis to mitigate the shortcomings of existing methods?