TIM-7070 v1

Maslej, N., Fattorini, L., Perrault, R., Parli, V., Reuel, A., Brynjolfsson, E., ... & Shoham, Y. (2024). Artificial intelligence index report 2024. arXiv preprint arXiv:2405.19522. https://arxiv.org/abs/2405.19522
The 2024 AI Index offers expanded data and insights on AI’s technical progress, societal impact, and global influence, marking its most comprehensive edition to date.

Waller, M., Rodrigues, O., Ah Lee, M. S., & Cocarascu, O. (2024). Bias mitigation methods: Applicability, legality, and recommendations for development. Journal of Artificial Intelligence Research, 81, 1043–1078. https://doi.org/10.1613/jair.1.16759
This paper critiques current bias mitigation methods in algorithmic decision-making, highlighting legal compliance issues and offering recommendations for fairer, regulation-aligned AI system development.

Juunior, A. O., Calvo-Rolle, J. L., & Leitao, P. (2024). Simulation on digital twin: Role of artificial intelligence and emergence of industrial metaverse. 2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE), Industrial Electronics (ISIE), 2024 IEEE 33rd International Symposium On, 1–6. https://doi.org/10.1109/ISIE54533.2024.10595676
This paper explores how Digital Twins integrates AI and simulation to enhance real-time system modeling, supporting monitoring, optimization, and Industrial Metaverse development while addressing key research challenges.

Gouiaa, R., & Huang, R. (2024). How do corporate governance, artificial intelligence, and innovation interact? Findings from different industries. Risk Governance & Control: Financial Markets & Institutions, 14(1), 35–52. https://doi.org/10.22495/rgcv14i1p3
This study explores how corporate governance attributes influence AI adoption and innovation, revealing industry-specific correlations and offering strategic insights for corporate decision-makers.

Colelough, B. C., & Regli, W. (2025). Neuro-symbolic ai in 2024: A systematic review. arXiv preprint arXiv:2501.05435. https://arxiv.org/abs/2501.05435
This article discusses how Neuro-Symbolic AI research has surged since 2020, focusing on learning and inference, while gaps in explainability, trust, and Meta-Cognition present key future challenges.

Perkins, M., & Roe, J. (2024). Generative ai tools in academic research: Applications and implications for qualitative and quantitative research methodologies. arXiv preprint arXiv:2408.06872. https://arxiv.org/abs/2408.06872
This study examines the impact of Generative Artificial Intelligence (GenAI) on academic research, focusing on its application to qualitative and quantitative data analysis.

Bentley, W. (2025). Being human and the emergence of artificial intelligence technology. Verbum et Ecclesia, 46(3), 1–6. https://doi.org/10.4102/ve.v46i3.3389
This article examines how AI reshapes human identity and roles, expanding Cornel W. du Toit's ideas and reflecting on the dialogue between science and religion.