1 citations · 2 across the 2 of their papers we have counts for
8 papers
OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning
Hantian Zhang, Xu Chu, Abolfazl Asudeh +1
Machine learning (ML) is increasingly being used to make decisions in our society. ML models, however, can be unfair to certain demographic groups (e.g., African Americans or femal…
MithraDetective: A System for Cherry-picked Trendlines Detection
Yoko Nagafuchi, Yin Lin, Kaushal Mamgain +5
Given a data set, misleading conclusions can be drawn from it by cherry-picking selected samples. One important class of conclusions is a trend derived from a data set of values ov…
Fair Active Learning
Hadis Anahideh, Abolfazl Asudeh, Saravanan Thirumuruganathan
Machine learning (ML) is increasingly being used in high-stakes applications impacting society. Therefore, it is of critical importance that ML models do not propagate discriminati…
Fair Active Learning
Hadis Anahideh, Abolfazl Asudeh, Saravanan Thirumuruganathan
Machine learning (ML) is increasingly being used in high-stakes applications impacting society. Therefore, it is of critical importance that ML models do not propagate discriminati…
Responsible Scoring Mechanisms Through Function Sampling
Abolfazl Asudeh, H. V. Jagadish
Human decision-makers often receive assistance from data-driven algorithmic systems that provide a score for evaluating objects, including individuals. The scores are generated by…
QR2: A Third-party Query Reranking Service Over Web Databases
Yeshwanth D. Gunasekaran, Abolfazl Asudeh, Sona Hasani +3
The ranked retrieval model has rapidly become the de-facto way for search query processing in web databases. Despite the extensive efforts on designing better ranking mechanisms, i…