Challenging Scientific Categorizations Through Dispute Learning DOI Creative Commons
Renaud Fabre, Patrice Bellot, D. Egret

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(4), P. 2241 - 2241

Published: Feb. 19, 2025

Scientific dispute and scholarly debate have traditionally served as mechanisms for arbitrating between competing scientific categorizations. However, current AI technologies lack both the ethical framework technical capabilities to handle adversarial reasoning inherent in discourse effectively. This creates a ‘categorization conundrum’ where new knowledge emerges from opaque black-box systems while simultaneously introducing unresolved vulnerabilities errors attacks. Our research addresses this challenge by examining how preserve enhance human dispute’s vital role creation, development, resolution of categorization, supported traceable assistance. Building on our previous work, which introduced GRAPHYP—a multiverse hypergraph representation opinion profiles derived multimodal web-based documentary traces—we present three key findings. First, we demonstrate that standardizing concepts methods through ‘Dispute Learning’ not only expands range pathways categorization but also enables identification GRAPHYP model extensions. These extensions accommodate additional forms contexts, guided novel philosophical methodological frameworks. Second, GRAPHYP’s support graph-based visualization provides access broad spectrum practical applications decidable challenging categorizations, illustrate selected case studies. Third, introduce hybrid analytical approach combining probabilistic possibilistic methods, applicable diverse classical data types. We identify by-products examine their epistemological implications. discussion standardized representations documented uses highlights enhanced value structured brings elicit differential categorizations discourse.

Language: Английский

Challenging Scientific Categorizations Through Dispute Learning DOI Creative Commons
Renaud Fabre, Patrice Bellot, D. Egret

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(4), P. 2241 - 2241

Published: Feb. 19, 2025

Scientific dispute and scholarly debate have traditionally served as mechanisms for arbitrating between competing scientific categorizations. However, current AI technologies lack both the ethical framework technical capabilities to handle adversarial reasoning inherent in discourse effectively. This creates a ‘categorization conundrum’ where new knowledge emerges from opaque black-box systems while simultaneously introducing unresolved vulnerabilities errors attacks. Our research addresses this challenge by examining how preserve enhance human dispute’s vital role creation, development, resolution of categorization, supported traceable assistance. Building on our previous work, which introduced GRAPHYP—a multiverse hypergraph representation opinion profiles derived multimodal web-based documentary traces—we present three key findings. First, we demonstrate that standardizing concepts methods through ‘Dispute Learning’ not only expands range pathways categorization but also enables identification GRAPHYP model extensions. These extensions accommodate additional forms contexts, guided novel philosophical methodological frameworks. Second, GRAPHYP’s support graph-based visualization provides access broad spectrum practical applications decidable challenging categorizations, illustrate selected case studies. Third, introduce hybrid analytical approach combining probabilistic possibilistic methods, applicable diverse classical data types. We identify by-products examine their epistemological implications. discussion standardized representations documented uses highlights enhanced value structured brings elicit differential categorizations discourse.

Language: Английский

Citations

0