Human Centered Machine Learning

Human-centered machine learning (HCML) prioritizes the integration of human values, needs, and feedback into the design and application of machine learning systems. Current research emphasizes developing methods to mitigate bias, ensure fairness, and enhance transparency in AI decision-making, often employing techniques like inductive message passing networks and multimodal learning approaches to analyze diverse data sources. This focus is driven by the need to create AI systems that are not only accurate and efficient but also ethically sound and beneficial to society, impacting fields ranging from healthcare (e.g., gait analysis) to recruitment and user interface design.