AI-Powered Collective Decision-Making Systems and the Future Trends

This paper presents an in-depth survey of the current state and future trends of artificial intelligence (AI) in augmenting collective decision-making processes. At the intersection of AI and group dynamics, this research explores how advanced AI technologies such as machine learning, reinforcement learning, natural language processing, predictive analytics, and swarm intelligence, revolutionize the way collective decisions are made in various sectors, including business, healthcare, governance, and finance. We delve into the theoretical foundations of collective decision-making, highlighting the pivotal role of AI in enhancing human judgment, overcoming cognitive limitations, and facilitating consensus in group settings. The paper examines the application of specific AI methodologies like decision trees, ensemble methods, Bayesian approaches, optimization algorithms, and simulation models in diverse decision-making scenarios. Real-world case studies are presented to illustrate the practical implications and transformative impact of AI in fields such as business strategy, healthcare decision support, financial market predictions, and public policy formulation. We address the challenges and limitations inherent in AI-driven decision systems, such as data quality, ethical concerns, biases, transparency, and integration with human processes. The abstract concludes by discussing emerging trends and future directions in AI collective decision-making, including advancements in explainable AI, ethical AI governance, integration of quantum computing, democratisation of decision-making, continuous learning in AI systems, and cross-disciplinary applications, underscoring the potential of AI to significantly enhance the efficacy and equity of collective decision-making across various domains.