Intellige: A User-Facing Model Explainer for Narrative Explanations
- URL: http://arxiv.org/abs/2105.12941v1
- Date: Thu, 27 May 2021 05:11:47 GMT
- Title: Intellige: A User-Facing Model Explainer for Narrative Explanations
- Authors: Jilei Yang, Diana Negoescu, Parvez Ahammad
- Abstract summary: We propose Intellige, a user-facing model explainer that creates user-digestible interpretations and insights.
Intellige builds an end-to-end pipeline from machine learning platforms to end user platforms.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Predictive machine learning models often lack interpretability, resulting in
low trust from model end users despite having high predictive performance.
While many model interpretation approaches return top important features to
help interpret model predictions, these top features may not be well-organized
or intuitive to end users, which limits model adoption rates. In this paper, we
propose Intellige, a user-facing model explainer that creates user-digestible
interpretations and insights reflecting the rationale behind model predictions.
Intellige builds an end-to-end pipeline from machine learning platforms to end
user platforms, and provides users with an interface for implementing model
interpretation approaches and for customizing narrative insights. Intellige is
a platform consisting of four components: Model Importer, Model Interpreter,
Narrative Generator, and Narrative Exporter. We describe these components, and
then demonstrate the effectiveness of Intellige through use cases at LinkedIn.
Quantitative performance analyses indicate that Intellige's narrative insights
lead to lifts in adoption rates of predictive model recommendations, as well as
to increases in downstream key metrics such as revenue when compared to
previous approaches, while qualitative analyses indicate positive feedback from
end users.
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