Generative AI can brainstorm objectives, but needs human expertise for decision quality

Tech Xplore — AI & ML•Tuesday, November 11, 2025 at 9:03:03 PM

The study published in Decision Analysis highlights the dual role of generative AI in organizational and policy decision-making. It can effectively brainstorm viable objectives, yet its outputs lack the necessary quality unless supplemented by human expertise. This finding is crucial as it emphasizes the ongoing need for human oversight in AI-driven processes, ensuring that the objectives set forth are not only viable but also of high quality. The implications of this research extend to various sectors that increasingly rely on AI for decision-making, reinforcing the idea that technology should augment rather than replace human judgment.

— via World Pulse Now AI Editorial System

Read Original

Was this article worth reading? Share it

Recommended Readings

arXiv — cs.CV2 days ago

Generative AI in Map-Making: A Technical Exploration and Its Implications for Cartographers

PositiveArtificial Intelligence

The article discusses the integration of generative AI in map-making, highlighting its potential to automate and democratize the process traditionally reliant on Geographic Information Systems (GIS). Despite advancements, generative AI models face challenges in creating accurate maps due to limitations in spatial composition and semantic layout. The authors present a model that generates precise maps in controlled styles, validated through user studies with professional cartographers, emphasizing the implications of generative AI in the field.

Read full article

via arXiv — cs.CV

arXiv — cs.LG2 days ago

Shifting Work Patterns with Generative AI

NeutralArtificial Intelligence

A recent field experiment involving 66 firms and 7,137 knowledge workers demonstrated the impact of a generative AI tool on work patterns. The study found that workers who used the AI tool, integrated into their existing applications for email, meetings, and writing, saved an average of two hours per week on email tasks. Additionally, these workers reduced their time spent working outside regular hours. However, the experiment did not reveal any significant changes in the quantity or composition of tasks performed by the workers.

Read full article

via arXiv — cs.LG

arXiv — cs.LG2 days ago

When to Stop Federated Learning: Zero-Shot Generation of Synthetic Validation Data with Generative AI for Early Stopping

PositiveArtificial Intelligence

Federated Learning (FL) allows collaborative model training across decentralized devices while ensuring data privacy. Traditional FL methods often run for a set number of global rounds, which can lead to unnecessary computations when optimal performance is achieved earlier. To improve efficiency, a new zero-shot synthetic validation framework using generative AI has been introduced to monitor model performance and determine early stopping points, potentially reducing training rounds by up to 74% while maintaining accuracy within 1% of the optimal.

Read full article

via arXiv — cs.LG