BibTex format

author = {Cocarascu, O and Stylianou, A and Cyras, K and Toni, F},
doi = {10.3233/FAIA200377},
pages = {2449--2456},
publisher = {IOS Press},
title = {Data-empowered argumentation for dialectically explainable predictions},
url = {},
year = {2020}

RIS format (EndNote, RefMan)

AB - Today’s AI landscape is permeated by plentiful data anddominated by powerful data-centric methods with the potential toimpact a wide range of human sectors. Yet, in some settings this po-tential is hindered by these data-centric AI methods being mostlyopaque. Considerable efforts are currently being devoted to defin-ing methods for explaining black-box techniques in some settings,while the use of transparent methods is being advocated in others,especially when high-stake decisions are involved, as in healthcareand the practice of law. In this paper we advocate a novel transpar-ent paradigm of Data-Empowered Argumentation (DEAr in short)for dialectically explainable predictions. DEAr relies upon the ex-traction of argumentation debates from data, so that the dialecticaloutcomes of these debates amount to predictions (e.g. classifications)that can be explained dialectically. The argumentation debates con-sist of (data) arguments which may not be linguistic in general butmay nonetheless be deemed to be ‘arguments’ in that they are dialec-tically related, for instance by disagreeing on data labels. We illus-trate and experiment with the DEAr paradigm in three settings, mak-ing use, respectively, of categorical data, (annotated) images and text.We show empirically that DEAr is competitive with another transpar-ent model, namely decision trees (DTs), while also providing natu-rally dialectical explanations.
AU - Cocarascu,O
AU - Stylianou,A
AU - Cyras,K
AU - Toni,F
DO - 10.3233/FAIA200377
EP - 2456
PB - IOS Press
PY - 2020///
SP - 2449
TI - Data-empowered argumentation for dialectically explainable predictions
UR -
UR -
UR -
ER -