Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Cruickshank, Iain"'
Large language models (LLMs) have enhanced our ability to rapidly analyze and classify unstructured natural language data. However, concerns regarding cost, network limitations, and security constraints have posed challenges for their integration int
Externí odkaz:
http://arxiv.org/abs/2408.08217
Large Language Models (LLMs) have demonstrated remarkable capabilities in executing tasks based on natural language queries. However, these models, trained on curated datasets, inherently embody biases ranging from racial to national and gender biase
Externí odkaz:
http://arxiv.org/abs/2407.17688
News will have biases so long as people have opinions. However, as social media becomes the primary entry point for news and partisan gaps increase, it is increasingly important for informed citizens to be able to identify bias. People will be able t
Externí odkaz:
http://arxiv.org/abs/2406.10965
Stance detection of social media text is a key component of many real-world applications like evaluating marketing campaigns, evaluating political policies or candidates, or evaluating information environments. However, creating automatic stance labe
Externí odkaz:
http://arxiv.org/abs/2403.03334
Autor:
Benson, Seth P., Cruickshank, Iain J.
Media bias has been extensively studied by both social and computational sciences. However, current work still has a large reliance on human input and subjective assessment to label biases. This is especially true for cable news research. To address
Externí odkaz:
http://arxiv.org/abs/2310.09166
Stance classification, the task of predicting the viewpoint of an author on a subject of interest, has long been a focal point of research in domains ranging from social science to machine learning. Current stance detection methods rely predominantly
Externí odkaz:
http://arxiv.org/abs/2309.13734
With the rise of phenomena like `fake news' and the growth of heavily-biased media ecosystems, there has been increased attention on understanding and evaluating media bias. Of particular note in the evaluation of media bias is writing style bias, wh
Externí odkaz:
http://arxiv.org/abs/2305.13098
Autor:
Berenbeim, Alexander M., Cruickshank, Iain J., Jha, Susmit, Thomson, Robert H., Bastian, Nathaniel D.
Quantitative characterizations and estimations of uncertainty are of fundamental importance in optimization and decision-making processes. Herein, we propose intuitive scores, which we call certainty and doubt, that can be used in both a Bayesian and
Externí odkaz:
http://arxiv.org/abs/2303.14568
Autor:
Maffey, Katherine R., Dotterrer, Kyle, Niemann, Jennifer, Cruickshank, Iain, Lewis, Grace A., Kästner, Christian
Many organizations seek to ensure that machine learning (ML) and artificial intelligence (AI) systems work as intended in production but currently do not have a cohesive methodology in place to do so. To fill this gap, we propose MLTE (Machine Learni
Externí odkaz:
http://arxiv.org/abs/2303.01998
Publikováno v:
Phalanx, 2023 Oct 01. 56(3), 24-31.
Externí odkaz:
https://www.jstor.org/stable/27255019