By Abhimanyu Bharti
Exit polls have long been a cornerstone of understanding electoral dynamics, providing insights into voter behaviour and preferences. Traditionally, these polls relied on manual data collection methods, presenting challenges in terms of accuracy and inclusivity.
The traditional exit poll process involves selection of constituent assembly, training of surveyors, surveyors stationed at polling booths, manually collecting data using pen and paper (now digitally), through interviews with voters leaving the premises. While this method offered valuable insights, its limitations in sample size and speed are evident. There are sample biases due to constituency selection, manual data collection and entry. The process is slow as in-person interviews are conducted. There is also inherent shyness of certain voter groups to talk, and hence the older methods have remained less inclusive.
In this scenario, Artificial Intelligence (AI) has become a game-changer. AI algorithms have significantly transformed and can enhance the accuracy of these polls. AI offers real-time data collection through innovative methods. AI can be used across the whole process of conducting exit polls; starting from sampling, surveying and data collection, to analysis and prediction, leading to much efficiency, and accurate results. Sampling via neural networks ensures correct representation of complex social hierarchy, adapting samples to evolving patterns and thereby keeping it relevant. This also helps identify homogeneous groups within the dataset, allowing for targeted sampling from different voter groups. Thus it ensures a diverse and representative sample. Automated Voice-AI callings used to collect voter’s opinions on the day of polling allows collection of voters’ opinions in larger volume and has made the exit polls effortless.
AI in poll surveys enhanced accessibility too, allowing a broader demographic to participate, including those with limited literacy as well in remote areas and reaching the shy voters. Also the convenience of responding via voice, eliminated the need for manual data entry, reducing the likelihood of errors in the survey data. It also allows updating the surveys by regular calling and getting a sense of changing sentiments among voters. Conducting monthly polls with an initial dataset further refines the AI models, allowing the model to learn and adapt to predict accurate results. This iterative process help improve the models continuously and enhances their predictive capabilities.
Additionally, real-time data collection and analysis through voice AI accelerates the survey process, providing quicker, realtime and more accurate results. Moreover, the natural language processing capabilities of voice AI captured nuanced responses, contributing to a more comprehensive understanding of voter sentiments. This survey has a drastically low turnaround time, consists of a high quantity of diverse samples, reduces error by checks and balances at every step, provides detailed demographic analysis and all of these at a very effective cost. Given, there is no manual data entry, data collection, interpretation of responses, or manual analysis involved, the results are quite objective and neutral without human biases, and thus reflects public opinion more accurately.
While AI presents numerous advantages, challenges such as algorithmic bias, privacy concerns, and the potential for manipulation must be acknowledged. The AI-enabled exit polls use natural language processing. Where India is a country with multiple dialects and training the NLP with each dialect to process the data in the right way can be a big challenge. Also, AI models are only as good as the data they are trained on. Understanding human behaviour, especially in voting scenarios, is complex. AI might not always capture the nuances of decision-making or the last-minute changes in voters’ choices. Also, factors like social media, news, or external events could affect voter behaviour post-exit poll interviews, making predictions less accurate.
Looking ahead, the future of AI-driven exit polls holds exciting possibilities. Continued advancements in machine learning, increased integration with emerging technologies, and refined algorithms promise even greater accuracy and reliability.
AI has undeniably revolutionised the landscape of exit polls, ushering in a new era of accuracy, efficiency, and inclusivity. As technology evolves, it is imperative to navigate challenges responsibly, ensuring that AI continues to be a valuable tool in understanding the voice of the people in democratic processes.
The author is co-founder,The School of Politics
