Exploring and understanding the motivations behind black-box model predictions is becoming essential in many different applications. X-PLAIN is an interactive tool that allows human-in-the-loop inspection of the reasons behind model predictions. Its support for the local analysis of individual predictions enables users to inspect the local behavior of different classifiers and compare the knowledge different classifiers are exploiting for their prediction. The interactive exploration of prediction explanation provides actionable insights for both trusting and validating model predictions and, in case of unexpected behaviors, for debugging and improving the model itself.

Bring Your Own Data to X-PLAIN / Pastor, E.; Baralis, E.. - In: PROCEEDINGS - ACM-SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA. - ISSN 0730-8078. - (2020), pp. 2805-2808. ((Intervento presentato al convegno 2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020 tenutosi a usa nel 2020 [10.1145/3318464.3384710].

Bring Your Own Data to X-PLAIN

Pastor E.;Baralis E.
2020

Abstract

Exploring and understanding the motivations behind black-box model predictions is becoming essential in many different applications. X-PLAIN is an interactive tool that allows human-in-the-loop inspection of the reasons behind model predictions. Its support for the local analysis of individual predictions enables users to inspect the local behavior of different classifiers and compare the knowledge different classifiers are exploiting for their prediction. The interactive exploration of prediction explanation provides actionable insights for both trusting and validating model predictions and, in case of unexpected behaviors, for debugging and improving the model itself.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2845124