Discount Quality for Responsible Data Science
Human-in-the-Loop for Quality Data
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Beschreibung
This open access book presents a “discount”-quality approach to data preparation for scientific data analysis, a critical prerequisite in modern applications such as data analysis projects, Artificial Intelligence, and Machine Learning. It discusses advanced techniques for fostering responsible data science, i.e., designing sustainable data analysis pipelines based on Human-In-The-Loop (HITL) approaches to achieve high-quality data. It investigates developing task- and context-driven sustainable approaches for data preparation, drawing on methods and theories to reduce annotations and processing space and time, and considering the estimation of the necessary human computing effort and the requirements of the specific task. The contributions address key aspects related to data ecosystems, data preparation pipelines, data quality evaluation and improvement, on-demand approaches to data preparation, data enrichment, and human factors in data preparation.
This open access book presents a “discount”-quality approach to data preparation for scientific data analysis, a critical prerequisite in modern applications such as data analysis projects, Artificial Intelligence, and Machine Learning. It discusses advanced techniques for fostering responsible data science, i.e., designing sustainable data analysis pipelines based on Human-In-The-Loop (HITL) approaches to achieve high-quality data. It investigates developing task- and context-driven sustainable approaches for data preparation, drawing on methods and theories to reduce annotations and processing space and time, and considering the estimation of the necessary human computing effort and the requirements of the specific task. The contributions address key aspects related to data ecosystems, data preparation pipelines, data quality evaluation and improvement, on-demand approaches to data preparation, data enrichment, and human factors in data preparation.
This book is open access, which means that you have free and unlimited access Examines data ecosystems, data preparation, data quality evaluation, information visualization and visual analytics Develops sustainable approaches to data preparation for researchers and companies in the Data-centric AI sector Highlights an approach that considers sustainability for the data preparation, incl. efficiency & socio-economic aspects
Autor*in
Barbara Pernici
Themen in »Discount Quality for Responsible Data Science«
Open Access data quality data preparation energy efficiency human computing effort HITL data enrichment data-centric AI