Anacleto Correia
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Associate Professor
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About
Dr. Anacleto Correia is an Associate Professor at the Portuguese Naval School in Portugal. His research focuses on artificial intelligence, corporate governance, innovation management, and business policy. Recent articles include "Artificial intelligence to enhance corporate governance: A conceptual framework" and "Stimulate innovation in organizations." His work aims to advance the understanding of innovation governance in practical settings.
Recent Grants
Grant: Close
MEDEA - The Mediterranean & Black Sea Security Practitioners’ Network
Open Date: 2022-12-01
Close Date: 2024-11-01
Grant: Close
MEDEA- The Mediterranean & Black Sea Security Practitioners’ Network
Open Date: 2022-12-01
Close Date: 2024-11-01
Grant: Close
MarLEM: Marine Logistics Engineering and Management
Open Date: 2019-11-01
Close Date: 2023-04-01
Grant: Close
MarLEM: Marine Logistics Engineering and Management
Open Date: 2019-11-01
Close Date: 2023-04-01
Grant: Close
ARESIBO - Augmented Reality Enriched Situation awareness for Border security
Open Date: 2019-04-01
Close Date: 2020-04-01
Articles (3)
In this preliminary study, we explore the novel intersection of corporate governance (CG) and artificial intelligence (AI), addressing the crucial question: How can AI be leveraged to enhance ethical and transparent decision-making within the corporate environment? Drawing from current studies on organizational governance, AI ethics, and data science, our research raises the curtain on the potential of AI in augmenting traditional governance mechanisms, while also scrutinizing the ethical quandaries and challenges it may pose. We propose a novel conceptual framework, rooted in the principles of separation of ownership and control, and data ethics, to be underpinned and validated, in the future, through an empirical study. Given the current inception stage of the study, we expect the results will illustrate a significant positive impact of AI on CG effectiveness, particularly in enhancing transparency and fostering ethical decision-making. We also propose future studies to be done as a mix of econometric and machine learning methods to empirically test the framework with datasets gathered over a period of years.
Year:
2023
Year:
2021
Year:
2020
Collaborators (1)
Pedro B. Água
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