Could AI have Prevented the Exit Poll Mess in India?
As the vote counting for the grand 2024 Indian Lok Sabha elections began on June 4, many people resorted to social media platforms like X to express their dissatisfaction with the exit poll results, calling out their inaccuracies and calling for more reliable methods. Various pollsters predicted the incumbent National Democratic Alliance (NDA) would secure 350-400 seats. However, the alliance only managed to secure 293 seats, with the BJP winning 240 seats. With exit polls having strayed way off the mark even in the past, could AI emerge as a potential game-changer here? In Comes AI “Instead of directly questioning individuals—which can introduce social desirability bias—we extrapolate their opinions from their online interaction. This method minimises bias and eliminates the need for lengthy and tedious interviews,” said Matteo Serafino, chief data scientist at KCore Analytics, in an exclusive interaction with AIM. The data research company predicted voter preferences using AI collected from people’s online activities on social media—what they were reading, writing, and reacting to. This data, collected in real-time, was then analysed using AI algorithms that take into account various factors that could affect elections, such as inflation, thereby providing more accurate predictions. Streamlining Data “We compile a basket of users with identified preferences, akin to a sample in traditional polling. This data is then integrated with the macroeconomic and historical data through a reweighting process, leading to our final insights. Crucially, this is all done while preserving user privacy,” said Serafino. KCore converts unstructured input, including text, audio, and images, into structured data for analysis using techniques from network theory, natural language processing (NLP), and computer vision. It employed Graph Neural Networks (GNN) for predictions and Bidirectional Encoder Representations from Transformers (BERT) for sentiment analysis. It’s Not New Though Numerous AI startups have already developed models to forecast elections. Expert.ai, a software startup specialising in natural language processing, employed AI to examine social media remarks regarding Donald Trump and Joe Biden in the months leading up to the 2020 US elections. The company’s AI interprets the emotions conveyed in social media posts and predicts how these will translate into votes. Using NLP, it classifies the attitude expressed in posts using over 80 distinct emotional categories. Another AI company, Unanimous.ai, used its programme to survey people in the United States in September 2020. It united vast groups of individuals via the internet, forming a “swarm intelligence” that magnified the members’ collective knowledge and ideas. Unanimous.ai correctly predicted the presidential election victor in 11 states. Outdated Traditional Methods In a typical exit poll, voters are interviewed as they leave the building after voting. Surveyors are trained and stationed at polling booths, and data is traditionally collected using pen and paper (now digitally). However, the accuracy of the results can vary depending on many factors. These include sample size, demographic representation, structured questionnaires, random telephone or in-person interviews for fairness, and data compilation in a timely manner. Thus, results can be distorted. Yeshwant Deshmukh, the founder of C-Voter, one of India’s major polling organisations, identified sample sizes and limited resources as problems. He claims that polling in India is as complex as polling in a diverse region like the European Union, but “pollsters don’t have that kind of budget”. With such challenges, AI-driven exit polls are the key to having close-to-accurate results. “In the future, traditional pollsters will integrate AI algorithms with their existing data. Given the continuous decline in response rates for traditional surveys, a gradual shift is anticipated, although the current industry mindset may resist such change,” said Serafino.
GNN and BERT are some of the techniques for predicting and performing sentiment analysis for exit polls.
GNN and BERT are some of the techniques for predicting and performing sentiment analysis for exit polls.
["AI (Artificial Intelligence)", "AI Tool", "Data Science", "Machine Learning", "OpenAI"]
["OpenAI", "AI", "neural network", "sentiment analysis", "ML", "Machine Learning", "Transformers", "computer vision", "NLP", "Ray", "Aim", "analytics", "Data Science", "AI (Artificial Intelligence)", "AI Tool"]
["AI", "ML", "neural network", "NLP", "computer vision", "analytics", "Aim", "Ray", "Transformers", "sentiment analysis"]
Anshul Vipat is a tech aficionado, enthusiastic about the latest innovations in the digital world. He also holds keen interest in traveling, exploring and cooking