Pedreschi D.; Giannotti F.; Guidotti R.; Monreale A.; Pappalardo L.; Ruggieri S.; Turini F.
Università di Pisa, Pisa, Italy; ISTI-CNR, Pisa, Italy; Università di Pisa, Pisa, Italy; ISTI-CNR, Pisa, Italy; Università di Pisa, Pisa, Italy; ISTI-CNR, Pisa, Italy; Università di Pisa, Pisa, Italy
Black box systems for automated decision making, often based on machine learning over (big) data, map a user's features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases hidden in the algorithms, due to human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We introduce the local-to-global framework for black box explanation, a novel approach with promising early results, which paves the road for a wide spectrum of future developments along three dimensions:(i) the language for expressing explanations in terms of highly expressive logic-based rules, with a statistical and causal interpretation;(ii) the inference of local explanations aimed at revealing the logic of the decision adopted for a specific instance by querying and auditing the black box in the vicinity of the target instance;(iii), the bottom-up generalization of the many local explanations into simple global ones, with algorithms that optimize the quality and comprehensibility of explanations.
Open the black box, Explanation, Interpretable models
Ruggieri Salvatore, Turini Franco, Pedreschi Dino, Monreale Anna, Giannotti Fosca, Pappalardo Luca, Guidotti Riccardo
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 397173
Year: 2018
Type: Rapporto tecnico
Creation: 2019-01-02 11:29:04.000
Last update: 2020-12-18 19:31:05.000
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:397173