As ESG principles and cutting-edge digital innovations intersect in the creative economy, strategic leadership is changing in ways never before imaginable. This chapter discusses the role of Explainable Artificial Intelligence (XAI) in supporting ESG-driven decision intelligence by allowing a leader to make transparent, accountable, and data-informed decisions. Based on the Resource Based View and Dynamic Capabilities Theory, the chapter advances a conceptual framework that combines explainability, ESG performance measures, and strategic leadership capabilities. The conversation focuses on the benefits of explainable AI for governance, stakeholders' trust, sustainability, and organizational resilience in the creative sector. The chapter also provides future research directions in the context of responsible human-centric AI leaders in the changing digital and sustainability landscapes.
Duggirala Aravind, R. N. Ravikumar, A. Rahmath Nisha· 0 citations
The current world economies are in a highly volatile framework characterized by thick interdependencies, and quick changing risk factors. The classical econometric models with their assumption of the stasis and restrictive data granularity cannot predict systemic shocks and timely interventions. This chapter provides a technical review of how Artificial Intelligence can increase economic resilience via predictive governance. It is a conceptualization of AI as a multi-layered analytical architecture that achieves high-frequency data streams, machine-learning prediction models, and policy optimization structures. The capabilities of deep learning, reinforcement learning, and network-based models to detect the emergent signals, forecast macroeconomic anomalies and simulate the counterfactual policy outcomes are put under stress. Transparency in algorithms, interpretable models and ethics in macro-level applications are also discussed in the chapter. It can be argued that AI-enabled predictive governance is necessary to develop resilient, adaptive, sustainable economic systems.
Duggirala Aravind, Mohammed Waseequ Sheraz, N. V. Suresh et al.· Advances in computational in...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
