Human AI Collaboration in Decision-Making Auto Systems With AI

Authors

DOI:

https://doi.org/10.62802/b4z5p105

Keywords:

Human-AI Collaboration, Automated Decision-Making, Synergy, Ethical AI, Transparency, Human Oversight, Interdisciplinary Analysis, Responsible Implementation

Abstract

The integration of Artificial Intelligence (AI) with human decision-making processes has led to the emergence of advanced automated systems designed to enhance efficiency, accuracy, and adaptability across various domains. This research investigates the collaborative dynamics between human decision-makers and AI-driven systems, focusing on their synergistic potential in automated decision-making frameworks. By combining human intuition and expertise with the computational power of AI, these systems enable optimized decision-making in complex environments. The study explores applications across industries such as healthcare, finance, and autonomous vehicles, highlighting their impact on productivity and innovation. Challenges, including ethical considerations, transparency, and trust, are critically analyzed to ensure responsible implementation. This research further examines how human oversight complements AI capabilities, fostering robust systems that balance automation with accountability. Through interdisciplinary analysis and empirical evidence, the study underscores the transformative potential of human-AI collaboration in reshaping decision-making paradigms. The findings contribute to the ongoing discourse on the future of human-machine synergy, offering actionable insights for policymakers, industry leaders, and researchers.

References

Feng, K., & Chaspari, T. (2024). A Pilot Study on Clinician-AI Collaboration in Diagnosing Depression from Speech. arXiv preprint arXiv:2410.18297.

Lin, J., Tomlin, N., Andreas, J., & Eisner, J. (2024). Decision-oriented dialogue for human-ai collaboration. Transactions of the Association for Computational Linguistics, 12, 892-911.

Lu, Z., Wang, D., & Yin, M. (2024). Does more advice help? the effects of second opinions in AI-assisted decision making. Proceedings of the ACM on Human-Computer Interaction, 8(CSCW1), 1-31.

Ren, C., Pardos, Z., & Li, Z. (2024). Human-AI Collaboration Increases Skill Tagging Speed but Degrades Accuracy. arXiv preprint arXiv:2403.02259.

Salimzadeh, S., He, G., & Gadiraju, U. (2024, May). Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-17).

Sarker, I. H. (2024). AI-driven cybersecurity and threat intelligence: cyber automation, intelligent decision-making and explainability. Springer Nature.

Tariq, S., Chhetri, M. B., Nepal, S., & Paris, C. (2024). A2C: A Modular Multi-stage Collaborative Decision Framework for Human-AI Teams. arXiv preprint arXiv:2401.14432.

Veitch, E., Dybvik, H., Steinert, M., & Alsos, O. A. (2024). Collaborative work with highly automated marine navigation systems. Computer Supported Cooperative Work (CSCW), 33(1), 7-38.

Wang, B., Yuan, T., & Rau, P. L. P. (2024). Effects of Explanation Strategy and Autonomy of Explainable AI on Human–AI Collaborative Decision-making. International Journal of Social Robotics, 1-20.

Zhang, S., Yu, J., Xu, X., Yin, C., Lu, Y., Yao, B., ... & Wang, D. (2024, May). Rethinking human-ai collaboration in complex medical decision making: A case study in sepsis diagnosis. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-18).

frontpage

Published

2024-12-17