Search Results

We explore the potential of generative AI text-to-image models to help designers efficiently craft unique, representative, and demographically diverse anthropographics that visualize data about people. Currently, creating data-driven iconic images to ...

We present the Center for Inclusive Computing's data collection and visualization system, which enables computing departments to track and visualize their enrollment and course outcome data intersectionally and longitudinally. The system tracks the ...

Anthropographics are human-shaped visualizations that aim to emphasize the human importance of datasets and the people behind them. However, current anthropographics tend to employ homogeneous human shapes to encode data about diverse demographic ...

Urban districts are central to questions of sustainability, especially in a developing nation like India. India is divided into 28 states and 8 union territories, each of which has a number of districts. Usually, the policies in these districts are ...

Automated Decision Systems (ADS) are being used to inform important decisions in government services. Concerns regarding discrimination in ADS have led to the rise of bias mitigation techniques, or data science practices that measure and adjust for ...

Computing education research (CER) has used demographic data to understand learners’ identities, backgrounds, and contexts for efforts such as culturally-responsive computing. Prior work indicates that failing to elucidate and critically engage with the ...

Most proposed algorithmic fairness techniques require access to demographic data in order to make performance comparisons and standardizations across groups, however this data is largely unavailable in practice, hindering the widespread adoption of ...

As calls for fair and unbiased algorithmic systems increase, so too does the number of individuals working on algorithmic fairness in industry. However, these practitioners often do not have access to the demographic data they feel they need to detect ...

In this paper, we propose a recommender system approach which considers contextual information from users and items in order to improve the accuracy of a neighborhood-based collaborative filtering algorithm. One advantage of our model is the possibility ...

This paper proposes a hybrid recommender algorithm which integrates a set of different user's inputs into a unified and generic latent factor model to improve prediction accuracy. The technique can exploit users' demographics, such as age, gender and ...

The purpose of this paper is to examine how singular value decomposition (SVD) and demographic information can improve the performance of plain collaborative filtering (CF) algorithms. After a brief introduction to SVD, where the method is explained and ...

Abstract: The University of California, Los Angeles (UCLA) has launched a global teleradiology project to provide subspecialist consultation to remote regions with the intent to deliver higher quality health care and contain the rising cost of health ...