The use of Large Language Models (LLMs) has proven to be a tool that could help in the automatic detection of sexism. Previous studies have shown that these models contain biases that do not accurately reflect reality, especially for minority groups. Despite various efforts to improve the detection of sexist content, this task remains a significant challenge due to its subjective nature and the biases present in automated models. We explore the capabilities of different LLMs to detect sexism in social media text using the EXIST 2024 tweet dataset. It includes annotations from six distinct profiles for each tweet, allowing us to evaluate to what extent LLMs can mimic these groups' perceptions in sexism detection. Additionally, we analyze the demographic biases present in the models and conduct a statistical analysis to identify which demographic characteristics (age, gender) contribute most effectively to this task. Our results show that, while LLMs can to some extent detect sexism when considering the overall opinion of populations, they do not accurately replicate the diversity of perceptions among different demographic groups. This highlights the need for better-calibrated models that account for the diversity of perspectives across different populations.

There are not one but two dimensions of bias that can be revealed through the study of large AI models: not only bias in training data or the products of an AI, but also bias in society, such as disparity in employment or health outcomes between different demographic groups. Often training data and AI output is biased for or against certain demographics (i.e. older white people are overrepresented in image datasets), but sometimes large AI models accurately illustrate biases in the real world (i.e. young black men being disproportionately viewed as threatening). These social disparities often appear in image generation AI outputs in the form of 'marked' features, where some feature of an individual or setting is a social marker of disparity, and prompts both humans and AI systems to treat subjects that are marked in this way as exceptional and requiring special treatment. Generative AI has proven to be very sensitive to such marked features, to the extent of over-emphasising them and thus often exacerbating social biases. I briefly discuss how we can use complex prompts to image generation AI to investigate either dimension of bias, emphasising how we can probe the large language models underlying image generation AI through, for example, automated sentiment analysis of the text prompts used to generate images.

When we talk of Customer Experience, the first thought probably coming to mind is about businesses reaching out to Gen Y and Gen Z. Nothing wrong in that, except that there's an equally important customer cohort that offers a huge opportunity for enterprises to enhance CX or Net Promoter Scores (NPS) – senior citizens! This demographic profile, comprising people 60 years and older, is what we refer to as senior citizens. In India, the number of senior citizens stands at approximately 140 million, and is projected to rise significantly over the next decade. With their global population projected to hit one billion by 2030 and spending power close to $15 trillion, rest assured this group will have huge impact on business going ahead. New vistas expanding CX for seniors Of late, we have begun to see remarkable changes in senior citizens.

Men and women have different shopping preferences, and different strategies are needed to attract them into your store. Even among genders, age is an essential factor. A middle-aged woman, for example, will have a different in-store shopping behavior from a teenage girl. V-Count's Demographic Analysis solutions enable businesses to separate their visitors into groups based on their age and gender data. When you know the demographic profile of your visitors, you are better equipped to provide them with a personalized shopping experience.

Men and women have different shopping preferences, and different strategies are needed to attract them into your store. Even among genders, age is an essential factor. A middle-aged woman, for example, will have a different in-store shopping behavior from a teenage girl. V-Count's Demographic Analysis solutions enable businesses to separate their visitors into groups based on their age and gender data. When you know the demographic profile of your visitors, you are better equipped to provide them with a personalized shopping experience.

Understanding the demographics of users of online social networks has important applications for health, marketing, and public messaging. In this paper, we predict the demographics of Twitter users based on whom they follow. Whereas most prior approaches rely on a supervised learning approach, in which individual users are labeled with demographics, we instead create a distantly labeled dataset by collecting audience measurement data for 1,500 websites (e.g., 50% of visitors to gizmodo.com are estimated to have a bachelor's degree). We then fit a regression model to predict these demographics using information about the followers of each website on Twitter. The resulting average held-out correlation is .77 across six different variables (gender, age, ethnicity, education, income, and child status). We additionally validate the model on a smaller set of Twitter users labeled individually for ethnicity and gender, finding performance that is surprisingly competitive with a fully supervised approach.