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pro vyhledávání: '"Bamgbose, Oluwanifemi"'
In this study, we introduce the application of causal disparity analysis to unveil intricate relationships and causal pathways between sensitive attributes and the targeted outcomes within real-world observational data. Our methodology involves emplo
Externí odkaz:
http://arxiv.org/abs/2407.02702
Autor:
Raza, Shaina, Bamgbose, Oluwanifemi, Ghuge, Shardul, Tavakol, Fatemeh, Reji, Deepak John, Bashir, Syed Raza
Large Language Models (LLMs) have advanced various Natural Language Processing (NLP) tasks, such as text generation and translation, among others. However, these models often generate text that can perpetuate biases. Existing approaches to mitigate t
Externí odkaz:
http://arxiv.org/abs/2404.01399
Autor:
Raza, Shaina, Khan, Tahniat, Chatrath, Veronica, Paulen-Patterson, Drai, Rahman, Mizanur, Bamgbose, Oluwanifemi
In today's technologically driven world, the rapid spread of fake news, particularly during critical events like elections, poses a growing threat to the integrity of information. To tackle this challenge head-on, we introduce FakeWatch, a comprehens
Externí odkaz:
http://arxiv.org/abs/2403.09858
In today's technologically driven world, the spread of fake news, particularly during crucial events such as elections, presents an increasing challenge to the integrity of information. To address this challenge, we introduce FakeWatch ElectionShield
Externí odkaz:
http://arxiv.org/abs/2312.03730
As the use of large language models (LLMs) increases within society, as does the risk of their misuse. Appropriate safeguards must be in place to ensure LLM outputs uphold the ethical standards of society, highlighting the positive role that artifici
Externí odkaz:
http://arxiv.org/abs/2310.18333
Autor:
Raza, Shaina, Bamgbose, Oluwanifemi, Chatrath, Veronica, Ghuge, Shardul, Sidyakin, Yan, Muaad, Abdullah Y
Bias detection in text is crucial for combating the spread of negative stereotypes, misinformation, and biased decision-making. Traditional language models frequently face challenges in generalizing beyond their training data and are typically design
Externí odkaz:
http://arxiv.org/abs/2310.00347
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