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Found 6 papers, 2 papers with code
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.
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.
