Cultural Analytics: Methods, Biases, and Interpretability in the Humanities

Neema Amani U.

Faculty of Business and Management Kampala International University Uganda

                                                                     ABSTRACT
This study examines the evolution of Cultural Analytics (CA) as an interdisciplinary framework that integrates computational, quantitative, and interpretive methods to analyze large-scale cultural data within the humanities. Emerging at the intersection of digitization and data science, CA expands traditional humanities inquiry by enabling the identification of patterns, networks, and temporal dynamics across vast corpora of texts, images, and other cultural artefacts. The paper critically evaluates methodological foundations, including corpus construction, data visualization, and quantitative techniques such as text mining, stylometry, and network analysis, while emphasizing the importance of data provenance, validity, and reproducibility. A central focus of the study is the role of bias both in datasets and algorithms and its implications for representation, interpretation, and knowledge production. It highlights how cultural, historical, and institutional factors shape data availability and selection, thereby influencing analytical outcomes and potentially reinforcing existing inequalities. The paper further explores interpretability and explainability as essential components of responsible cultural analytics, examining model transparency, human-centered evaluation, and the communicative role of visualizations. Ethical considerations, including privacy, consent, cultural sensitivity, and risks of misrepresentation or dual-use, are also interrogated within the context of digital scholarship. By synthesizing theoretical and practical perspectives, the study identifies key challenges and opportunities for advancing CA as a rigorous, reflexive, and ethically grounded approach. Ultimately, it argues for a balanced integration of computational methods and humanistic interpretation to enhance both the scope and depth of cultural inquiry in the digital age.

Keywords: Cultural Analytics, Digital Humanities, Algorithmic Bias, Interpretability and Explainability, and Data Provenance.

CITE AS: Neema Amani U. (2026). Cultural Analytics: Methods, Biases, and Interpretability in the
Humanities. IDOSR JOURNAL OF HUMANITIES AND SOCIAL SCIENCES 11(1): 47-57.
https://doi.org/10.59298/IDOSRJHSS/2026/1114757