The history of artificial intelligence (AI) research can be traced back to the 1950s, when scientists and mathematicians began exploring the possibility of developing machines that could mimic human intelligence. In the decades since, AI has gone through periods of heightened expectations and hype as well as periods where funding and interest faded, described as "AI winters." However, in recent years AI has seen a major resurgence, driven by key factors like the exponential growth in computing power, the availability of vast amounts of digital data, and algorithmic breakthroughs in machine learning techniques.
The modern era of AI has enabled systems to achieve human-level performance in specialized tasks like playing chess and Go, as well as surpassing human capabilities in many narrow applications. For example, machine learning systems can now reliably transcribe speech, translate between languages, and identify objects in images more accurately than humans. The advent of deep learning methods based on neural networks has been a driving force behind these advances by allowing systems to learn from large datasets rather than being explicitly programmed with rules. The abundance of data, availability of specialized hardware like GPUs, and enhancements in machine learning algorithms have all contributed to the recent success of AI.
While much of the focus has been on commercial applications of AI in areas like digital assistants, self-driving vehicles, fraud detection, and predictive analytics, AI tools and techniques also hold great promise for augmenting and enhancing social science research. Political science, in particular, shares many features with tasks and domains where AI has thrived, including the need to extract insights from large troves of textual data, modeling complex human systems, and forecasting outcomes based on historical evidence. As such, there has been a rapid growth in the application of AI methods in political science over the past decade.
AI's Growing Use in Political Science Research
Political science has embraced computational techniques since the 1950s, with early examples including content analysis of political texts, election forecasting models, and computer simulations of political events. The use of AI techniques in political science gained momentum in the 1990s and 2000s with the adoption of machine learning methods for tasks like classification and prediction. For instance, research in the early 2000s showed that machine learning algorithms could predict Supreme Court decisions and legislative roll call votes with accuracy rivaling human experts (Evans et al., 2002; Katz, 2006).
In recent years, there has been an exponential growth in the application of more advanced AI techniques in political science research. The number of published papers mentioning the use of machine learning in political science grew from around 5 per year in 2010 to over 175 in 2018 (Serrano et al., 2019). This surge of interest has been enabled by the increasing availability of digital data relevant to politics, such as text corpora, administrative records, and survey data. Much of the early focus was on predicting political outcomes, with applications in election forecasting, conflict early warning systems, and classification of political texts (Tolochko & Arbatov, 2021).
More recently, political scientists have tapped into cutting-edge AI methods like natural language processing, neural networks, and reinforcement learning for diverse research aims. Examples include:
Using neural networks to model the dynamics between economic conditions and political instability across countries (Munger et al., 2019)
Applying natural language processing to extract policy positions of candidates from text and speeches (Jung et al., 2021)
Analyzing social media data with machine learning to predict geopolitical events like Arab Spring protests (Breuer et al., 2015)
Using reinforcement learning algorithms to understand how different political strategies emerge through repeated interactions (Erkol et al., 2022)
Leveraging machine learning for causal inference strategies to estimate unbiased causal effects from observational data (Knutsen et al., 2021)
This proliferation of AI-based research reflects wider recognition of the value that automated methods can provide for analyzing complex political phenomena, identifying relationships in large-scale data, and generating insights that challenge existing theories. AI has moved from a peripheral set of tools to a core component of the methodological toolkit in many subfields of political science.
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