{"ID":2877144,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.20701","arxiv_id":"2508.20701","title":"Transparent Semantic Spaces: A Categorical Approach to Explainable Word Embeddings","abstract":"The paper introduces a novel framework based on category theory to enhance the explainability of artificial intelligence systems, particularly focusing on word embeddings. Key topics include the construction of categories $\\mathcal{L}_T$ and $\\mathcal{P}_T$, providing schematic representations of the semantics of a text $ T $, and reframing the selection of the element with maximum probability as a categorical notion. Additionally, the monoidal category $\\mathcal{P}_T$ is constructed to visualize various methods of extracting semantic information from $T$, offering a dimension-agnostic definition of semantic spaces reliant solely on information within the text. Furthermore, the paper defines the categories of configurations Conf and word embeddings $\\mathcal{Emb}$, accompanied by the concept of divergence as a decoration on $\\mathcal{Emb}$. It establishes a mathematically precise method for comparing word embeddings, demonstrating the equivalence between the GloVe and Word2Vec algorithms and the metric MDS algorithm, transitioning from neural network algorithms (black box) to a transparent framework. Finally, the paper presents a mathematical approach to computing biases before embedding and offers insights on mitigating biases at the semantic space level, advancing the field of explainable artificial intelligence.","short_abstract":"The paper introduces a novel framework based on category theory to enhance the explainability of artificial intelligence systems, particularly focusing on word embeddings. Key topics include the construction of categories $\\mathcal{L}_T$ and $\\mathcal{P}_T$, providing schematic representations of the semantics of a tex...","url_abs":"https://arxiv.org/abs/2508.20701","url_pdf":"https://arxiv.org/pdf/2508.20701v1","authors":"[\"Ares Fabregat-Hernández\",\"Javier Palanca\",\"Vicent Botti\"]","published":"2025-08-28T12:19:34Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.CL\",\"math.CT\"]","methods":"[]","has_code":false}