Human-AI Collaboration: Enhancing Decision-Making in Critical Sectors
DOI:
https://doi.org/10.4314/ggb6wr66Keywords:
Human-AI collaboration, Decision-making, Critical sectors, Artificial intelligence, Predictive analyticsAbstract
The integration of Artificial Intelligence (AI) into critical sectors such as healthcare, finance, security, and manufacturing has transformed decision-making processes. Human-AI collaboration leverages the strengths of both human intuition and machine intelligence to enhance accuracy, efficiency, and reliability in decision-making. AI systems provide data-driven insights, predictive analytics, and automation, while human expertise ensures ethical considerations, contextual understanding, and adaptability. This synergy improves risk assessment, crisis management, and strategic planning, ultimately leading to more informed and effective decisions. However, challenges such as trust, transparency, and bias in AI models must be addressed to maximize the benefits of human-AI collaboration. This paper explores the impact, benefits, and challenges of integrating AI with human decision-making across critical sectors.
Downloads
Published
Issue
Section
How to Cite
Most read articles by the same author(s)
- Enefiok Archibong Etuk, Omankwu, Obinnaya Chinecherem Beloved, Spiking Neural Networks (SNNs): A Path towards Brain-Inspired AI , Communication In Physical Sciences: Vol. 12 No. 2 (2025): VOLUME 12 ISSUE 2
Similar Articles
- Moses Oluwasegun Odewale, Moses Olagoke Odejobi, Olanrewaju Oluwaseun Ajayi, Advanced RF Optimization Techniques for Enhancing Coverage, Throughput, and Quality of Service in LTE and 5G Networks , Communication In Physical Sciences: Vol. 8 No. 4 (2022): VOLUME 8 ISSUE 4
- Fortune Meka, A Comparative Analysis of Sensitivity and Specificity of LASSO Logistic Regression and Random Forest Using Sonar Data , Communication In Physical Sciences: Vol. 13 No. 8 (2026): Volume 13, Issue 8
- A.O Obioha, Spatial Variability of key climate and air quality parameters across some Nigerian cities , Communication In Physical Sciences: Vol. 12 No. 5 (2025): VOLUME 12 ISSUE 5
- Confidence Ifeoma Odoh, Nweze Rosemary Chika Nweze, Ukamaka Victoria Maduahonwu, Development of an Enhanced Predictive Maintenance Models for Industrial Systems using Deep Learning Techniques , Communication In Physical Sciences: Vol. 13 No. 1 (2026): VOLUME 13 ISSUE 1
- Richard Jewo Bebekah, Malajiya Ibrahim Alhaji Saleh, Aliyu Mohammed, Yusuf Tanko, Modulatory Effect of L-carnitine on Red Blood Cell and Indices in Testicular Ischaemic-Reperfusion in Wistar Rats , Communication In Physical Sciences: Vol. 10 No. 2 (2023): VOLUME 10 ISSUE 2
- Moses Oluwasegun Odewale, Moses Olagoke Odejobi, Olanrewaju Oluwaseun Ajayi, Machine Learning-Based Predictive Congestion Management and Dynamic Resource Allocation in 5G Networks. , Communication In Physical Sciences: Vol. 9 No. 4 (2023): VOLUME 9 ISSUE 4
- Lambe Mutalub Adesina, Joseph Ibrahim Kuta Joshua, Ogunbiyi Olalekan, Abdul-Waheed Musa, Monsurat Balogun, Jimada-Ojuolape Bilkisu, Implementation of Random Forest and Support Vector Machine Models for Predictive Failure Detection in Power Transmission Equipment , Communication In Physical Sciences: Vol. 13 No. 10 (2026): Volume 13 Issue 10
- Victor O. Ikpeazu, Amaku James Friday, Kalu . K. Igwe, Ifeanyi E. Otuokere, Repositioning the Bioactive Compounds Isolated from Bauhinia Galpinii Leaves as Potential Inhibitors Against Human Immunodeficiency Virus (HIV) II Protease Through Application of In Silico Studies , Communication In Physical Sciences: Vol. 6 No. 1 (2020): VOLUME 6 ISSUE 1
- Faith Osaretin Osabuohien, Review of the Environmental Impact of Polymer Degradation , Communication In Physical Sciences: Vol. 2 No. 1 (2017): VOLUME 2 ISSUE 1
- Y. Buhari, A review on the prevalence of Human Giardiasis in some selected States in Nigeria , Communication In Physical Sciences: Vol. 5 No. 1 (2020): VOLUME 5 ISSUE 1
You may also start an advanced similarity search for this article.



