Search Results for author:
Found 11 papers, 5 papers with code
Classifying Conspiratorial Narratives At Scale: False Alarms and Erroneous Connections
Online discussions frequently involve conspiracy theories, which can contribute to the proliferation of belief in them.
Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems
Despite the benefits of personalizing items and information tailored to users' needs, it has been found that recommender systems tend to introduce biases that favor popular items or certain categories of items, and dominant user groups.
HungerGist: An Interpretable Predictive Model for Food Insecurity
The escalating food insecurity in Africa, caused by factors such as war, climate change, and poverty, demonstrates the critical need for advanced early warning systems.
VISPUR: Visual Aids for Identifying and Interpreting Spurious Associations in Data-Driven Decisions
Through an expert interview and a controlled user experiment, our qualitative and quantitative results demonstrate that the proposed "de-paradox" workflow and the designed visual analytic system are effective in helping human users to identify and understand spurious associations, as well as to make accountable causal decisions.
Tribe or Not? Critical Inspection of Group Differences Using TribalGram
With the rise of AI and data mining techniques, group profiling and group-level analysis have been increasingly used in many domains including policy making and direct marketing.
ESCAPE: Countering Systematic Errors from Machine's Blind Spots via Interactive Visual Analysis
Classification models learn to generalize the associations between data samples and their target classes.
Proceedings of KDD 2020 Workshop on Data-driven Humanitarian Mapping: Harnessing Human-Machine Intelligence for High-Stake Public Policy and Resilience Planning
Despite these growing perils, there remains a notable paucity of data science research to scientifically inform equitable public policy decisions for improving the livelihood of at-risk populations.
Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models
We investigate the impact of political ideology biases in training data.
FairSight: Visual Analytics for Fairness in Decision Making
Data-driven decision making related to individuals has become increasingly pervasive, but the issue concerning the potential discrimination has been raised by recent studies.
CASTNet: Community-Attentive Spatio-Temporal Networks for Opioid Overdose Forecasting
Motivated by these observations, we propose a novel spatio-temporal predictive model for opioid overdose forecasting by leveraging the spatio-temporal patterns of crime incidents.
Recipe recommendation using ingredient networks
The recording and sharing of cooking recipes, a human activity dating back thousands of years, naturally became an early and prominent social use of the web.
Social and Information Networks Physics and Society H.2.8
