Case Study: Empowering Corporate Governance İn Healthcare Bpos With Explainable AI İn Bangalore, India
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Abstract. Corporate governance is the system that guides how a company operates and makes decisions, ensuring transparency, fairness, and accountability in its management and leadership practices. This case study investigates how Explainable Artificial Intelligence (XAI) is influencing corporate governance practices in healthcare Business Process Outsourcing (BPO) firms based in Bangalore, India. Transparent and interpretable AI systems are increasingly viewed as tools that promote fairness, accountability, and ethical conduct in decision-making (Adadi & Berrada, 2018). XAI enables managers to understand how automated decisions are reached, supporting compliance with internal policies and external regulations (Gunning et al., 2019). By focusing on the intersection of technology and governance, explainability emerges not merely as a technical feature but as a key governance mechanism that strengthens oversight and builds organizational trust (Doshi-Velez & Kim, 2017). The rapid expansion of Artificial Intelligence within healthcare BPOs has improved efficiency but has also introduced new challenges related to transparency, ethical responsibility, and regulatory compliance (Guidotti et al., 2019). In many organizations, AI systems operate as opaque “black boxes,” offering limited insight into how outputs are generated (Samek et al., 2021). This opacity can weaken internal control mechanisms and complicate audit processes, especially in healthcare environments that depend on confidentiality, data accuracy, and ethical accountability. Explainable Artificial Intelligence addresses these challenges by making algorithmic processes more interpretable for human users. Transparent models allow administrators, compliance officers, and clients to build confidence in automated decision systems (Miller, 2019). The central research question guiding this study is: In what ways can XAI enhance corporate governance mechanisms within healthcare BPO organizations in Bangalore? The study posits that explainability serves a dual purpose—enhancing the technical reliability of AI while reinforcing governance and ethical integrity. Through an analysis of real-world applications across selected healthcare BPOs, this paper identifies how explainable systems foster accountability, ensure data integrity, and strengthen stakeholder trust in AI-enabled environments.
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