Year: 2025
Type: article
Abstract: The rapid developments in Artificial Intelligence (AI) and Machine Learning (ML) in the past decade have transformed numerous industries extensively through optimization and automation of decision-making. From manufacturing and medicine to finance and urban development, AI and ML technologies have proved themselves capable of streamlining operations, optimizing functional efficiency, and reducing human intervention. But despite the ubiquity of AI and ML, the field of research on achieving the full potential of the technologies, especially in real-time decision-making, interpretability, and ethics, still remains very large. Recent studies have shown that AI can automate repetitive tasks, from input of data, fraud detection, and customer service, to playing a role in more complex decision-making situations, from predictive finance analytics to personalized healthcare. But the gap lies in the balance between AI-automated functions and human input, especially where risky cases are involved, such as in medicine and autonomous vehicles. Moreover, issues with explaining AI models limit their large-scale deployment in sensitive decision-making areas.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408865482