Data Politics: Blending AI and Political Processes

Data has become increasingly valuable today. It is the key to numerous information processes that shape our lives, driving advertising, the internet of things, artificial intelligence (AI), and even life-saving services such as weather information. Hence data can be utilized in a multitude of ways, including enhancing risk management. The sheer amount of information that users share on social media platforms, for instance, facilitates the gathering of intelligence, which then can be used to anticipate and mitigate political risk.

Merging AI and Politics in Risk Management

A key tool to collect and process data on social media platforms is AI. Spending on the development of AI across the world has risen during the last five years. Between 2014 and 2019, the United States has spent the most on AI technologies, investing $65,735 million, followed by China ($14,381 million) and the UK ($3,761 million). Yet the emergence of AI tools and their autonomous problem-solving properties may generate skepticism among users and policymakers. A central concern surrounding AI is the automation of human work. One may wonder whether AI will replace humans in political and risk management decision-making. AI can facilitate politics-related human work by processing enormous amounts of data, far exceeding human capability. Human decision-making, however, continues to drive risk management. Moving forward, risk analysis can be made more accurate by blending the capabilities of humans and AI.

Unraveling the Case for Removing Human Work from Political Processes

While data will be at the very heart of decision-making in risk management, human input will continue to be indispensable. Researchers and analysts can design risk management projects by making decisions about what information the AI tool should process and how to interpret the AI tool’s output. This can result in human bias over what information to select or how to evaluate information. It also allows researchers and analysts to tailor a project to a client’s needs, giving humans decision-making power. Therefore, the collaboration between data scientists and subject matter experts in politics and policymaking permits a more comprehensive decision-making process that is supported by a large body of data evidence.

A Goal-Driven Blend: AI and Risk Assessment

AI-based risk solutions can help political election candidates understand voter sentiment around key concepts and voting issues for specific elections. Scraping and analyzing data from social media can assist candidates in gaining insight into public opinion and can craft campaign messaging accordingly. This can help campaigners to not only understand public sentiment but also to anticipate potential election risks and outcomes.

How does this differ from traditional polling to anticipate election outcomes? In poll-based forecasting, it is largely assumed that voters of specific age groups, geographical locations, education levels, and social classes make consistent voting decisions over a span of time. For instance, in the UK, people from the working class are more likely to vote for the British Labour Party. Polls indicated mostly correct election results in the UK in 1997, 2001, 2005, and 2010, yet they failed to do so in 1992 and 2015. The polling in 2015 included too many Labour voters but not enough Conservative voters to correctly predict the election outcome. Additionally, the social class of a voter increasingly loses significance for the voter’s decisions in an election. Therefore, voters’ decisions could potentially become more difficult to predict.

This is how AI-based risk management can enhance politicians’, lobbyists’ and other stakeholders’ understandings of public perception, regardless of social class. The benefit of social media is that it offers real-time insight into a wide-ranging demography of users. Social media is therefore a useful platform to explore public opinion. Moreover, AI can illustrate changes in public sentiment over time around key election topics, granting a better understanding of how citizens feel about a certain candidate or policy issue.

Conclusion: Expanding the Political Risk Management Scope Through AI

To summarize, AI utilizes quantitative methods in order to add a qualitative narrative to political risk management. By backing human interpretation skills with new AI-based data evidence, Global Risk Intelligence’s unique AI methodology expands the scope of political risk management.

Click here to view our AI-integrated Political Risk Solution.

About the Author

Yasemin Zeisl

Yasemin Zeisl earned her MSc in International Relations and Affairs from the London School of Economics and Political Science (LSE). Yasemin is fluent in German and English and possesses advanced Japanese language skills.

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