Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare
Abstract Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to
derive insights from clinical data and improve patient outcomes. However, these highly …
derive insights from clinical data and improve patient outcomes. However, these highly …
Deep learning in electron microscopy
JM Ede - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …
microscopy. This review paper offers a practical perspective aimed at developers with …
Towards accountability for machine learning datasets: Practices from software engineering and infrastructure
Datasets that power machine learning are often used, shared, and reused with little visibility
into the processes of deliberation that led to their creation. As artificial intelligence systems …
into the processes of deliberation that led to their creation. As artificial intelligence systems …
Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing
Rising concern for the societal implications of artificial intelligence systems has inspired a
wave of academic and journalistic literature in which deployed systems are audited for harm …
wave of academic and journalistic literature in which deployed systems are audited for harm …
Machine learning testing: Survey, landscapes and horizons
This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …
Software engineering for AI-based systems: a survey
AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …
Collaboration challenges in building ml-enabled systems: Communication, documentation, engineering, and process
The introduction of machine learning (ML) components in software projects has created the
need for software engineers to collaborate with data scientists and other specialists. While …
need for software engineers to collaborate with data scientists and other specialists. While …
Data validation for machine learning
N Polyzotis, M Zinkevich, S Roy… - … of machine learning …, 2019 - proceedings.mlsys.org
Abstract Machine learning is a powerful tool for gleaning knowledge from massive amounts
of data. While a great deal of machine learning research has focused on improving the …
of data. While a great deal of machine learning research has focused on improving the …
Coordinating Human and Machine Learning for Effective Organizational Learning.
With the rise of machine learning (ML), humans are no longer the only ones capable of
learning and contributing to an organization's stock of knowledge. We study how …
learning and contributing to an organization's stock of knowledge. We study how …
Towards CRISP-ML (Q): a machine learning process model with quality assurance methodology
S Studer, TB Bui, C Drescher, A Hanuschkin… - Machine learning and …, 2021 - mdpi.com
Machine learning is an established and frequently used technique in industry and
academia, but a standard process model to improve success and efficiency of machine …
academia, but a standard process model to improve success and efficiency of machine …