AI for Democracy

Elections are a cornerstone of political accountability in democracies, allowing voters to regularly assess representatives and either re-elect them or vote them out of office. However, fair elections alone are insufficient - voters also need access to objective information about policy contexts and incumbents' records. 

The rise of generative AI poses both risks and opportunities for accountability. Technologies like chatbots could flood the information ecosystem with manipulated content that distorts voters' perceptions. But they also offer new ways to provide high-quality, accessible political information. 

In this project, we investigate generative AI's dual effects on accountability. First, we examine whether surging misinformation and disinformation from AI systems could impair voters' capacity to hold governments to account. We analyse the volume and characteristics of generative content around elections and model its potential impacts on knowledge, trust, and voting behaviour. 

Second, we explore using AI responsibly to enhance accountability. Can tools like interactive chatbots give citizens convenient access to factual, impartial information about policies, economic conditions, and incumbent track records? We design and test AI systems to deliver such information and measure their impacts on accountability. 

Bridging computer science and political science, this research provides urgently needed insight into AI's emerging democratic influence. It aims to mitigate risks of manipulation while also developing novel AI applications to make accountability more robust. By furthering theoretical and practical understanding, we can help secure democracy in the age of increasingly powerful generative technologies. 

Recent research indicates a potential link between fiscal austerity and the rise of far-right politics.

Proposed mechanisms include declining compensation for those negatively impacted by economic liberalisation. However, findings on austerity's electoral effects are mixed. Some suggest austerity does not hurt incumbents electorally, while others find austerity-proposing governments less likely to stay in office. Variation may stem from different methods. There is also evidence that neoliberal ideology promotes individualised rather than systemic blame attribution for economic conditions.

Combining these insights, this project investigates blame attribution in political rhetoric justifying austerity. It examines whether responsibility framing differs between partisan perspectives and minister backgrounds. We utilise causal language models to identify types of blame attribution. Theoretical frameworks consider differential effects of austerity given variation in responsibility attribution. One hypothesis is partisan divergence in austerity justifications, though some claim policy convergence between right and left. Another examines ministerial background influence on framing despite preparation by permanent staff. Findings can reveal perceptions of fiscal consolidation causes, influential narratives in austerity discourse, and impacts of attribution framing. They will elucidate politicians’ rhetorical strategies when imposing unpopular policies. This project combines natural language processing, experimental methods, and comparative approaches to analyse essential questions on austerity and blame attribution. It aims to explain significant political developments and inform effective policymaking during fiscal consolidations.

Despite long-standing academic interest in political rhetoric, we lack systematic understanding of how politicians strategically employ language to shape policy agendas and outcomes. 

In this project, we investigate how politicians manipulate portrayed psychological distance to future events in speeches and pledges to direct attention toward their priority issues. Rather than mere rhetoric, this linguistic framing has tangible effects on subsequent government policies. 

To study this phenomenon, we develop new methods that extract measures of event horizon, resolution, urgency, certainty, and construal directly from text. Applying these tools to political rhetoric, we can assess differences in psychological distance and link them to policy impacts. 

The project will enhance theoretical knowledge of the strategic role of time and framing in political communication. It also provides an innovative framework for studying massive textual data to understand real-world agenda setting and policymaking. Our computational techniques offer transferable models for social scientists exploring language influence across contexts.