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InteractiveResource . 2023
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InteractiveResource . 2023
License: CC BY
Data sources: Datacite
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InteractiveResource . 2023
License: CC BY
Data sources: Datacite
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Introduction to Transparent Machine Learning

Authors: Lu, Haiping; Zhou, Shuo;

Introduction to Transparent Machine Learning

Abstract

This course aims to address the priority of transparency in responsible AI. We will study both transparent machine learning systems/models and transparent machine learning processes. We will adapt classical machine learning textbooks and materials under this framework to give a fresh treatment that will be more accessible for learners from multiple disciplines, including engineering, science, social sciences, medical science, and humanities. This will greatly complement the existing Responsible AI training landscape in the UK and beyond. Specifically, this course will recast selected contents in a leading textbook into the perspective of system and process transparency under a recent AI transparency framework from the Alan Turing Institute on "AI in Financial Services". Transparent machine learning systems will cover fully transparent machine learning models such as linear regression and "semi-transparent" machine learning models such as deep learning. Transparent machine learning processes will cover machine learning model evaluation and software development methodologies such as cross validation and software development life cycle. If you would like full access to the course materials and files please email us at skills@turing.ac.uk for more details.

Related Organizations
Keywords

Artificial Intelligence, Machine learning, ethical AI, Transparency

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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