Alina Munir

Papers

Total Citations

H-Index

About

Alina Munir is a rising researcher at the forefront of human-robot interaction (HRI) and explainable artificial intelligence (XAI). Her work centers on a critical question: how do robots’ explanations shape human perceptions, trust, and collaboration? In her highly cited 2022 paper, “Explain yourself! Effects of Explanations in Human-Robot Interaction,” Munir systematically investigates how robot decision-making justifications influence user trust and perceived reliability—a foundational contribution to the emerging field of transparent robotics. Garnering 23 citations in just two years, this work has already informed subsequent studies on human-robot communication and ethical AI design. Munir’s research bridges cognitive science and robotics, demonstrating that explanations are not merely technical outputs but social signals that can either strengthen or undermine human-robot partnerships. Her findings have practical implications for autonomous systems in healthcare, manufacturing, and service robotics, where user trust is paramount. As a young scholar, Munir is establishing herself as a key voice in making AI systems more interpretable and trustworthy, laying the groundwork for robots that can truly explain themselves.