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Found 14 papers, 5 papers with code
Automatic Identification of 5C Vaccine Behaviour on Social Media
Monitoring vaccine behaviour through social media can guide health policy.
Estimating Gender Completeness in Wikipedia
Gender imbalance in Wikipedia content is a known challenge which the editor community is actively addressing.
Identification of Regulatory Requirements Relevant to Business Processes: A Comparative Study on Generative AI, Embedding-based Ranking, Crowd and Expert-driven Methods
Organizations face the challenge of ensuring compliance with an increasing amount of requirements from various regulatory documents.
Data Bias Management
Due to the widespread use of data-powered systems in our everyday lives, concepts like bias and fairness gained significant attention among researchers and practitioners, in both industry and academia.
On the Impact of Data Quality on Image Classification Fairness
By adding noise to the original datasets, we can explore the relationship between the quality of the training data and the fairness of the output of the models trained on that data.
Perspectives on Large Language Models for Relevance Judgment
When asked, large language models (LLMs) like ChatGPT claim that they can assist with relevance judgments but it is not clear whether automated judgments can reliably be used in evaluations of retrieval systems.
Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?
Fact-checking is one of the effective solutions in fighting online misinformation.
Managing Bias in Human-Annotated Data: Moving Beyond Bias Removal
Due to the widespread use of data-powered systems in our everyday lives, the notions of bias and fairness gained significant attention among researchers and practitioners, in both industry and academia.
The Many Dimensions of Truthfulness: Crowdsourcing Misinformation Assessments on a Multidimensional Scale
We deploy a set of quality control mechanisms to ensure that the thousands of assessments collected on 180 publicly available fact-checked statements distributed over two datasets are of adequate quality, including a custom search engine used by the crowd workers to find web pages supporting their truthfulness assessments.
Can the Crowd Judge Truthfulness? A Longitudinal Study on Recent Misinformation about COVID-19
Our results show that: workers are able to detect and objectively categorize online (mis)information related to COVID-19; both crowdsourced and expert judgments can be transformed and aggregated to improve quality; worker background and other signals (e. g., source of information, behavior) impact the quality of the data.
The COVID-19 Infodemic: Can the Crowd Judge Recent Misinformation Objectively?
Misinformation is an ever increasing problem that is difficult to solve for the research community and has a negative impact on the society at large.
Proceedings of the KG-BIAS Workshop 2020 at AKBC 2020
The KG-BIAS 2020 workshop touches on biases and how they surface in knowledge graphs (KGs), biases in the source data that is used to create KGs, methods for measuring or remediating bias in KGs, but also identifying other biases such as how and which languages are represented in automatically constructed KGs or how personal KGs might incur inherent biases.
Can The Crowd Identify Misinformation Objectively? The Effects of Judgment Scale and Assessor's Background
Truthfulness judgments are a fundamental step in the process of fighting misinformation, as they are crucial to train and evaluate classifiers that automatically distinguish true and false statements.
FashionBrain Project: A Vision for Understanding Europe's Fashion Data Universe
A core business in the fashion industry is the understanding and prediction of customer needs and trends.
