Dongsoo Han

Dongsoo Han

The emergence of artificial intelligence and robotics is catalyzing a profound transformation in the nature of human labor, fueling a contentious debate about the future of employment. While prominent studies predict substantial job displacement due to automation, historical precedents from past technological revolutions suggest that innovation tends to expand, rather than shrink, the scope of economic activity and employment in the long run. This paper advances the thesis that the transition to an AI-civilization will fundamentally restructure the mechanisms of employment creation. We argue for a paradigm shift from a centralized model, where a limited number of organizations create jobs for the mass to a decentralized ecosystem where individuals are empowered to generate their own employment opportunities. This shift is enabled by AI-driven productivity augmentation, which dramatically lowers the barriers to creating economic value. Drawing on an analysis of economic history, contemporary data on labor market dynamics, and the growth of digital platforms, this paper posits that human-AI co-evolution will significantly increase individual productivity and open new frontiers of economic activity. We explore the implications of this structural transformation for education and workforce development, concluding that the focus must shift from rote knowledge accumulation to cultivating skills in human AI collaboration, creative problem-solving, and the design of novel economic domains. This paper contributes to the literature by offering a forward-looking framework that emphasizes the decentralizing potential of AI on labor markets, moving beyond the traditional displacement-versus-creation dichotomy.

Dongsoo Han

This study empirically investigates the impact of AI-augmented peer review systems on scientific productivity using panel data from OECD countries. While prior research has highlighted inefficiencies in traditional peer review, little empirical work has quantified the systemic impact of AI integration at the national level. We construct a novel AI Review Capability Index (AIRC) and examine its effects on research productivity, reproducibility, and innovation output. Using fixed-effects regression and structural equation modeling (SEM), we show that AI-assisted evaluation significantly enhances productivity and reduces variance in research quality. Results indicate that a one standard deviation increase in AIRC is associated with an 18-25% increase in scientific productivity, mediated through improvements in review efficiency and reproducibility. This paper provides the first cross-country empirical validation of AI-augmented scientific evaluation systems and contributes to the emerging literature on AI as a structural driver of knowledge production.

Omer Tariq, Muhammad Bilal, Muneeb Ul Hassan et al.

Data-driven inertial sequence learning has revolutionized navigation in GPS-denied environments, offering superior odometric resolution compared to traditional Bayesian methods. However, deep learning-based inertial tracking systems remain vulnerable to privacy breaches that can expose sensitive training data. \hl{Existing differential privacy solutions often compromise model performance by introducing excessive noise, particularly in high-frequency inertial measurements.} In this article, we propose ConvXformer, a hybrid architecture that fuses ConvNeXt blocks with Transformer encoders in a hierarchical structure for robust inertial navigation. We propose an efficient differential privacy mechanism incorporating adaptive gradient clipping and gradient-aligned noise injection (GANI) to protect sensitive information while ensuring model performance. Our framework leverages truncated singular value decomposition for gradient processing, enabling precise control over the privacy-utility trade-off. Comprehensive performance evaluations on benchmark datasets (OxIOD, RIDI, RoNIN) demonstrate that ConvXformer surpasses state-of-the-art methods, achieving more than 40% improvement in positioning accuracy while ensuring $(ε,δ)$-differential privacy guarantees. To validate real-world performance, we introduce the Mech-IO dataset, collected from the mechanical engineering building at KAIST, where intense magnetic fields from industrial equipment induce significant sensor perturbations. This demonstrated robustness under severe environmental distortions makes our framework well-suited for secure and intelligent navigation in cyber-physical systems.