Explainable Generative AI for Sustainable Human Capital Management

The fast adaptation of Generative Artificial Intelligence (GIA) into human capital management systems has changed organizational decision-making, workforce analytics, and plenty of management of sustainability. However, with the increasing complexity and impenetrability of generative models it raises important questions about transparency, fairness, accountability and stakeholder trust. This chapter reviews the role of explainable Generative AI in supporting sustainable human capital management by facilitating aspects such as improved interpretation, ethics compliance and participation of ESG driven workforce strategies in the business environment. Explainable AI mechanisms (including model interpretability frameworks, bias detection algorithms and systems for transparent reporting) help to build faith in AI assisted recruitment, assessment of performance, workforce planning and learning and development processes. By underlying the principle of 'explainability' in generative systems, organizations can mitigate risks related to algorithmic bias, breaches of data privacy and lack of transparency in the decision-making process. Furthermore, XGenAI facilitates sustainability of environmental, social and financial measurements (The ESG) by providing auditable insights of diversity measurements, sustainability meters and responsible governance practices. The study presents a conceptual framework for a conjunction of principles of explainability, human supervision and sustainability in intelligent human capital ecosystems. It extensively points out human-AI collaboration, regulatory compliance, among other, as fundamental pillars in the latter's responsible adoption. Ultimately, Explainable Generative AI not only leads to greater transparency and trust in the workforce but also tackle to the same strategy in terms of alignment of Artificial Intelligence driven talent strategies with environmental, social and governance long-term objectives. This way, organizations can meet resilient ethical sustainable workforce transformation in the changing digital economy.