Automated fact-checking to support professional practices: systematic literature review and meta-analysis
Fact-checking is a time-consuming process that automation can potentially make more
efficient. This study provides a comprehensive, multidisciplinary state of the art that …
efficient. This study provides a comprehensive, multidisciplinary state of the art that …
The effects of data quality on machine learning performance
L Budach, M Feuerpfeil, N Ihde, A Nathansen… - arXiv preprint arXiv …, 2022 - arxiv.org
Modern artificial intelligence (AI) applications require large quantities of training and test
data. This need creates critical challenges not only concerning the availability of such data …
data. This need creates critical challenges not only concerning the availability of such data …
An artificial intelligence life cycle: From conception to production
D De Silva, D Alahakoon - Patterns, 2022 - cell.com
This paper presents the" CDAC AI life cycle," a comprehensive life cycle for the design,
development, and deployment of artificial intelligence (AI) systems and solutions. It …
development, and deployment of artificial intelligence (AI) systems and solutions. It …
Data-iq: Characterizing subgroups with heterogeneous outcomes in tabular data
High model performance, on average, can hide that models may systematically
underperform on subgroups of the data. We consider the tabular setting, which surfaces the …
underperform on subgroups of the data. We consider the tabular setting, which surfaces the …
TRIAGE: Characterizing and auditing training data for improved regression
Data quality is crucial for robust machine learning algorithms, with the recent interest in data-
centric AI emphasizing the importance of training data characterization. However, current …
centric AI emphasizing the importance of training data characterization. However, current …
[HTML][HTML] A hybrid machine learning approach for the load prediction in the sustainable transition of district heating networks
Current district heating networks are undergoing a sustainable transition towards the 4 th
and 5 th generation of district heating networks, characterized by the integration of different …
and 5 th generation of district heating networks, characterized by the integration of different …
Reimagining synthetic tabular data generation through data-centric AI: A comprehensive benchmark
Synthetic data serves as an alternative in training machine learning models, particularly
when real-world data is limited or inaccessible. However, ensuring that synthetic data …
when real-world data is limited or inaccessible. However, ensuring that synthetic data …
Data smells: Categories, causes and consequences, and detection of suspicious data in ai-based systems
High data quality is fundamental for today's AI-based systems. However, although data
quality has been an object of research for decades, there is a clear lack of research on …
quality has been an object of research for decades, there is a clear lack of research on …
Data assimilation for urban stormwater and water quality simulations using deep reinforcement learning
Hydrological models have been used to understand the transportation of water quantity and
quality in drainage systems, and the stormwater management model (SWMM) is one of the …
quality in drainage systems, and the stormwater management model (SWMM) is one of the …
Advances in exploratory data analysis, visualisation and quality for data centric AI systems
It is widely accepted that data preparation is one of the most time-consuming steps of the
machine learning (ML) lifecycle. It is also one of the most important steps, as the quality of …
machine learning (ML) lifecycle. It is also one of the most important steps, as the quality of …