Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Zaki, Yasir"'
Technologies for recognizing facial attributes like race, gender, age, and emotion have several applications, such as surveillance, advertising content, sentiment analysis, and the study of demographic trends and social behaviors. Analyzing demograph
Externí odkaz:
http://arxiv.org/abs/2410.24148
A Longitudinal Analysis of Racial and Gender Bias in New York Times and Fox News Images and Articles
Autor:
Ibrahim, Hazem, AlDahoul, Nouar, Abbasi, Syed Mustafa Ali, Zaffar, Fareed, Rahwan, Talal, Zaki, Yasir
The manner in which different racial and gender groups are portrayed in news coverage plays a large role in shaping public opinion. As such, understanding how such groups are portrayed in news media is of notable societal value, and has thus been a s
Externí odkaz:
http://arxiv.org/abs/2410.21898
Recent breakthroughs in Large Language Models (LLMs) have led to their adoption across a wide range of tasks, ranging from code generation to machine translation and sentiment analysis, etc. Red teaming/Safety alignment efforts show that fine-tuning
Externí odkaz:
http://arxiv.org/abs/2409.15361
In recent years, we have witnessed myriad flavours of Mobile Network Aggregators (MNAs) which exploit the coverage footprint of a handful of base operators to provide global mobile connectivity. Under the MNA model, emerging operators reap the benefi
Externí odkaz:
http://arxiv.org/abs/2408.14923
As of 2022, about 2.78 billion people in developing countries do not have access to the Internet. Lack of Internet access hinders economic growth, educational opportunities, and access to information and services. Recent initiatives to ``connect the
Externí odkaz:
http://arxiv.org/abs/2407.01738
Large language models (LLMs) demonstrate impressive zero-shot and few-shot reasoning capabilities. Some propose that such capabilities can be improved through self-reflection, i.e., letting LLMs reflect on their own output to identify and correct mis
Externí odkaz:
http://arxiv.org/abs/2406.10400
Images are often termed as representations of perceived reality. As such, racial and gender biases in popular media imagery could play a vital role in shaping people's perceptions of society. While inquiries into such biases have examined the frequen
Externí odkaz:
http://arxiv.org/abs/2405.06404
A quarter of US adults regularly get their news from YouTube. Yet, despite the massive political content available on the platform, to date no classifier has been proposed to identify the political leaning of YouTube videos. To fill this gap, we prop
Externí odkaz:
http://arxiv.org/abs/2404.04261
Citations are widely considered in scientists' evaluation. As such, scientists may be incentivized to inflate their citation counts. While previous literature has examined self-citations and citation cartels, it remains unclear whether scientists can
Externí odkaz:
http://arxiv.org/abs/2402.04607
Text-to-image generative AI models such as Stable Diffusion are used daily by millions worldwide. However, the extent to which these models exhibit racial and gender stereotypes is not yet fully understood. Here, we document significant biases in Sta
Externí odkaz:
http://arxiv.org/abs/2402.01002