Explainable artificial intelligence: Difference between revisions

Revision as of 10:00, 17 July 2017

Revision as of 10:06, 17 July 2017

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It is asking the question of '''how''' algorithms arrive at the decisions they do. AI related algorithmic (supervised and unsupervised) practices work on a model of success that orientates towards some form of correct state with singular focus placed on an expected output e.g. an image recognition algorithm's level of success will be based on the algorithms ability to recognize certain objects, failure to do so will indicate that the algorithm requires further tuning. As the tuning level is dynamic, closely correlated to function refinement and training data-set, granular understanding of the underlying operational vectors is rarely introspected.

XAI aims to address this black-box approach and allow introspection of these dynamic systems tractable, allowing humans to understand how computational machines develop their own models for solving tasks.

== Definition ==

A universal definition of this term has yet to have been fully established however the DARPA XAI programme defines it's aim as the following:

The Darpa initiative defines XAI as the following:

"The Explainable AI (XAI) program aims to create a suite of machine learning techniques that:

* Produce more explainable models, while maintaining a high level of learning performance (prediction accuracy); and

* Enable human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners.<ref>{{cite web|title=Explainable Artificial Intelligence (XAI)|url=https://www.darpa.mil/program/explainable-artificial-intelligence|website=DARPA|publisher=DARPA|accessdate=17 July 2017}}</ref>