HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence
  for Digital Medicine
Rodrigo Agerri Iñigo Alonso Aitziber Atutxa Ander Berrondo Ainara Estarrona Iker Garcia-Ferrero Iakes Goenaga Koldo Gojenola Maite Oronoz Igor Perez-Tejedor German Rigau Anar Yeginbergenova
Providing high quality explanations for AI predictions based on machine
learning is a challenging and complex task. To work well it requires, among
other factors: selecting a proper level of generality/specificity of the
explanation; considering assumptions about the familiarity of the explanation
beneficiary with the AI task under consideration; referring to specific
elements that have contributed to the decision; making use of additional
knowledge (e.g. expert evidence) which might not be part of the prediction
process; and providing evidence supporting negative hypothesis. Finally, the
system needs to formulate the explanation in a clearly interpretable, and
possibly convincing, way. Given these considerations, ANTIDOTE fosters an
integrated vision of explainable AI, where low-level characteristics of the
deep learning process are combined with higher level schemes proper of the
human argumentation capacity. ANTIDOTE will exploit cross-disciplinary
competences in deep learning and argumentation to support a broader and
innovative view of explainable AI, where the need for high-quality explanations
for clinical cases deliberation is critical. As a first result of the project,
we publish the Antidote CasiMedicos dataset to facilitate research on
explainable AI in general, and argumentation in the medical domain in
particular.