Project TAILOR
Untying black boxes with clustering-based symbolic knowledge extraction Unmasking the Shadows: Leveraging Symbolic Knowledge Extraction to Discover Biases and Unfairness in Opaque Predictive Models On the Evaluation of the Symbolic Knowledge Extracted from Black Boxes Symbolic Knowledge Comparison: Metrics and Methodologies for Multi-Agent Systems Towards a Unified Model for Symbolic Knowledge Extraction with Hypercube-Based Methods Bottom-Up and Top-Down Workflows for Hypercube- and Clustering-based Knowledge Extractors A geometric framework for fairness Explainable Clustering with CREAM Unlocking Insights and Trust: The Value of Explainable Clustering Algorithms for Cognitive Agents Symbolic Knowledge-Extraction Evaluation Metrics: The FiRe Score Evaluation Metrics for Symbolic Knowledge Extracted from Machine Learning Black Boxes: A Discussion Paper Symbolic Knowledge Extraction from Opaque Machine Learning Predictors: GridREx & PEDRO
Toggle the left panel column.
Toggle the right panel column.
