Explain Results and Decisions to Users

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This practice addresses requirements
from the EU guidelines for trustworthy ML.
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Intent

Allow users to critically assess the results and decisions of the machine learning application, so they can accept them on an informed basis, or catch possible errors.

Motivation

Users are entitled to know the basis on which a decision that affects them was made.

Applicability

Explanations should be applied to any machine learning application.

Description

Machine learning systems involve highly complex between data, algorithms and models. As a result they are often difficult to understand, even for other experts.

In order to increase transparency and align the application with ethics guidelines, it is imperative to inform users on the reasons why a decision was made. For example, the EU GDPR law, as well as the Credit score in the USA, require the right to an explanation for automated decision making systems.

Not only may users be more accepting of decisions made by machine learning systems when they understand what the decision was based on, it also helps them to raise concerns when the explanation is unsatisfactory, or – in the extreme case – plain wrong. In turn this helps to improve machine learning systems to make better decisions, and provide improved explanations in the future.

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43 / 45 Governance This practice was not ranked.
Click to read more.
This practice addresses requirements
from the EU guidelines for trustworthy ML.
Click to read more.