%0 Conference Paper %1 10.1007/978-3-031-89274-5_9 %A Schembera, Björn %B Designing the Conceptual Landscape for a XAIR Validation Infrastructure %C Cham %D 2025 %E Al Machot, Fadi %E Horsch, Martin T. %E Scholze, Sebastian %I Springer Nature Switzerland %K myown metadata mathematics data cmcs rdm knowledge information dikw %P 122--132 %T Data, Information, Knowledge, Wisdom, and Explainable Artificial Intelligence %X The data -- information -- knowledge -- wisdom (DIKW)-hierarchy has become a standard framework for examining relationships among data, information, knowledge. This article explores the integration of DIKW with the FAIR and XAIR principles. These principles drive essential transformation processes within the DIKW model: the FAIR principles support the transition from data to information, while XAIR principles enable information to become actionable knowledge. Wisdom is only created particularly through ethical and scientific practices that transcend purely technical considerations. At each level, these transformations reflect distinct epistemic values: FAIR fosters technical knowledge about data, XAIR enables scientific knowledge, and ethics and scientific practices contribute to broader societal knowledge. AI technology plays a dual role, making research machine-readable and interpretable while simultaneously enhancing transformation processes through AI-driven techniques. %@ 978-3-031-89274-5

@inproceedings{10.1007/978-3-031-89274-5_9, abstract = {The data -- information -- knowledge -- wisdom (DIKW)-hierarchy has become a standard framework for examining relationships among data, information, knowledge. This article explores the integration of DIKW with the FAIR and XAIR principles. These principles drive essential transformation processes within the DIKW model: the FAIR principles support the transition from data to information, while XAIR principles enable information to become actionable knowledge. Wisdom is only created particularly through ethical and scientific practices that transcend purely technical considerations. At each level, these transformations reflect distinct epistemic values: FAIR fosters technical knowledge about data, XAIR enables scientific knowledge, and ethics and scientific practices contribute to broader societal knowledge. AI technology plays a dual role, making research machine-readable and interpretable while simultaneously enhancing transformation processes through AI-driven techniques.}, added-at = {2025-06-16T13:16:37.000+0200}, address = {Cham}, author = {Schembera, Bj{\"o}rn}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/23ecff39f867db785e094b0d08416ce5d/mathematik}, booktitle = {Designing the Conceptual Landscape for a XAIR Validation Infrastructure}, editor = {Al Machot, Fadi and Horsch, Martin T. and Scholze, Sebastian}, interhash = {367221966a522da95baec91523b1d5ad}, intrahash = {3ecff39f867db785e094b0d08416ce5d}, isbn = {978-3-031-89274-5}, keywords = {myown metadata mathematics data cmcs rdm knowledge information dikw}, pages = {122--132}, publisher = {Springer Nature Switzerland}, timestamp = {2025-06-16T13:16:37.000+0200}, title = {Data, Information, Knowledge, Wisdom, and Explainable Artificial Intelligence}, year = 2025 }