Machine Unlearning (MU) has emerged as a significant concept in the realm of Machine Learning (ML), particularly in its application within educational contexts. By systematically reviewing 42 peer-reviewed sources, the paper underscores MU’s ability to enhance privacy protection, increase resilience against adversarial attacks, mitigate systemic biases, and adapt to evolving educational needs. As educational systems increasingly rely on AI, these capabilities become vital in fostering trust amongst users.
While MU has garnered interest in various domains, its implementation in education remains underexplored. The implications of integrating MU into educational AI systems are profound; it not only aligns with the principles of Responsible AI but also promotes adaptability essential for a dynamic learning environment. This paper contributes a conceptual framework, the Machine Unlearning application architecture for Responsible and Adaptive AI (MU-RAAI), aiming to bridge the gap between MU technology and its untapped potential in educational practices.
👉 Pročitaj original: arXiv AI Papers
