𝗫𝗔𝗜 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝟮𝟬𝟮𝟱 - 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗧𝗿𝗮𝗰𝗸 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁: 🖥️ Explainable AI for Relational Learning ✏️ 𝗔𝘂𝘁𝗵𝗼𝗿𝘀: FRANCESCO GIANNINI @michelangelo diligenti 🔗 𝗟𝗲𝗮𝗿𝗻 𝗠𝗼𝗿𝗲 𝗛𝗲𝗿𝗲: https://lnkd.in/dCq4mYpg 𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁: In the world of machine learning, where entities are often seen as independent, relational learning stands out by recognizing the crucial connections between them. This field, equipped with powerful tools like Graph Neural Networks and Knowledge Graph Embeddings, offers a way to understand complex, interconnected data. However, a significant limitation persists: the “black box” nature of these methods, which obscures their underlying decision-making processes. This is where explainable and interpretable methods become essential. By shedding light on how these models work, we can gain valuable insights into the relationships within the data. Moreover, the inherent graphical structure of relational data provides a unique opportunity to develop eXplainable AI (XAI) methods that leverage this structure for interpretation. Despite its potential, this avenue remains largely unexplored in current XAI approaches. Bridging this gap is crucial. Developing interpretable-by-design models and effective XAI methodologies specifically for relational data and methods will not only enhance trust and understanding, but also unlock the full potential of relational learning across various domains. This involves establishing clear theoretical foundations and definitions for XAI in the context of relational learning, paving the way for more transparent and insightful analyses. #relational #learning #explainability #artificialintelligence #graphneuralnetworks #knowledgegraph #blackboxes #machinelearning #relationaldata
