In high-stakes arenas like healthcare, finance, and fraud detection, the cost of AI getting it wrong can be immense, from ethical fallout to real-world harm. This paper explores how Human-in-the-Loop (HITL) artificial intelligence offers a crucial safeguard in such scenarios by keeping humans actively involved in decision-making processes. We dive into how human oversight can make AI systems more accountable, interpretable, and adaptable to complex, real-world challenges. Through real-world case studies and established frameworks, we assess how HITL can curb algorithmic bias, promote fairness, and deliver better outcomes. But it’s not all smooth sailing: issues like cognitive overload, ambiguous roles, and scaling challenges are also part of the equation. To move forward, we outline key design principles and propose concrete evaluation metrics aimed at building HITL systems we can truly trust.
Keywords: AI ethics and accountability, Human-AI collaboration, explainable AI (XAI), high-risk decision-making, human-in-the-loop (HITL)
