International Workshop on Explainable Artificial Intelligence in Bioengineering (EAIB) in conjunction with IEEE BIBE 2022


EAIB2022 Call for Papers


Artificial intelligence is widely adopted in bioinformatics and bioengineering. As a matter of fact when the diagnosis or selection of therapy is no longer performed exclusively by the physician, but to a significant extent by artificial intelligence, decisions easily become nontransparent. The most common application of machine learning algorithms in the bioinformatics and bioengineering context is automatic clinical decision-making. For these tasks, these are several well-known algorithms (artificial neural networks, classifiers, etc.), which are tuned based on (labeled) samples to optimize the classification of unseen instances. A deep understanding of the mathematical details of the decision behind an Artificial intelligence algorithm may be possible for statistics or computer science domain experts. Clearly, when it comes to the fate of human beings, this “developer’s explanation” is not sufficient.

The shift from therapy-relevant decisions based on human knowledge to black-box-like computer algorithms makes the decision-making increasingly incomprehensible to medical staff and patients. This has been recognized in the issuance of guidelines, e.g., by the European Union or DARPA (USA), which emphasize the need for computer-based decisions to be transparent and in a form that can be communicated in an understandable way to medical personnel and patients. To address this problem, the concept of explainable artificial intelligence (XAI) is attracting scientific interest. XAI uses a representation of human knowledge, usually (a subset of) predicate logic, for its reasoning, deduction, and classification (diagnosis).

The aim of this workshop is to boost the research and industrial community in the proposal and development of methodologies aimed to (clearly) explain the clinical decisional process to non-domain experts.

Topic

Topics of interest include, but are not limited to:

  • Explainable artificial intelligence
  • Biomedical data mining
  • Formal methods in medicine
  • Model Checking in clinical contexts
  • Interpretable data mining
  • Biomedical knowledge representation
  • Biomedical knowledge discovery

Workshop Paper Submission

Detailed instructions for manuscript preparation can be found on the conference paper submission website. Only electronic submission will be accepted. Manuscripts may only be submitted in PDF format. A copyright form needs to be submitted upon acceptance of the paper and is not required at this stage. Please note that:

  • Workshop papers should be directly submitted to the workshop organizers;
  • Submitted work must not contain contents that have been published or are under consideration for publication by any other journal or conference;
  • Every paper accepted for publication must have attached to it at least one registration at the full member/non-member rate. Thus, for a paper for which all authors are students, one student author will be required to register at the full registration rate;
  • Manuscripts must be written in English and follow the instructions in the Manuscript Formatting and Templates page;
  • Please follow the instructions of the author kit provided by the IEEE CPS to upload and also register your camera-ready paper by the deadline. The page limit of camera-ready paper for Workshop Paper is 4 pages, including all figures, tables, and references. (No extra pages allowed).

Accepted Papers

Each accepted paper should be presented by one of the authors at the conference. At least one author of each accepted paper should be registered and pay the full registration fee to ensure the publication of their paper in the BIBE 2022 conference proceedings and IEEE Digital Library.

Important Dates

  • Submission deadline:  June 30, 2022
  • Notification of acceptance:  July 31, 2022
  • Camera-ready version due:  August 31, 2022

Organizing Committee

Francesco Mercaldo, University of Molise, Italy, francesco.mercaldo@unimol.it

Antonella Santone, University of Molise, Italy, antonella.santone@unimol.it