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Designing the Conceptual Landscape for a XAIR Validation Infrastructure

Manuscript submission and reviewing are done through EasyChair (submission link). Submit your manuscript by 19th October 2024 and, if accepted, the camera-ready files by 30th November 2024.

The proceedings will be published in the Springer series Lecture Notes in Networks and Systems (LNNS), and by the camera-ready stage latest, manuscripts must conform with the specifications for publication in that series. Upon submission, select the appropriate category for your manuscript:

1. Discussion of a core concept for explainable-AI-readiness; if you select this, your paper must include a discussion of literature definitions of a concept relevant to explainable-AI-readiness.
2. Surveying the landscape of multiple core concepts; if you select this, your paper must include an analysis of how or whether different definitions of these concepts (which must be relevant to explainable-AI-readiness) can be combined with each other.
3. Applied ontology techniques for visualizing or designing the conceptual landscape; if you select this, your paper must include an application of said techniques to part of the conceptual landscape relevant to explainable-AI-readiness.
4. Going beyond FAIR - what is insufficient about the FAIR principles? If you select this, your paper must include a discussion of requirements that are insufficiently addressed by the FAIR principles, so that they need to be supplemented, updated, or revised.

("Must include" means that part of the paper must be about this, not necessarily all of the paper.)

Use the LaTeX template and style files provided by Springer, and keep your manuscript at a minimum of eight pages (excluding references/appendices) and a maximum of twenty pages (including references/appendices). Also confer Springer's guidelines for proceedings authors.

Submission link: https://easychair.org/my/conference?conf=dclxvi2024

AI4Work is funded from the EC's Horizon Europe research and innovation programme under GA no. 101135990.
BatCAT is funded from the EC's Horizon Europe research and innovation programme under GA no. 101137725.