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Found 11 papers, 2 papers with code
A Deep Ensemble Framework for Fake News Detection and Multi-Class Classification of Short Political Statements
Fake news, rumor, incorrect information, and misinformation detection are nowadays crucial issues as these might have serious consequences for our social fabrics.
FairBranch: Fairness Conflict Correction on Task-group Branches for Fair Multi-Task Learning
The generalization capacity of Multi-Task Learning (MTL) becomes limited when unrelated tasks negatively impact each other by updating shared parameters with conflicting gradients, resulting in negative transfer and a reduction in MTL accuracy compared to single-task learning (STL).
Multi-dimensional discrimination in Law and Machine Learning -- A comparative overview
AI-driven decision-making can lead to discrimination against certain individuals or social groups based on protected characteristics/attributes such as race, gender, or age.
Learning to Teach Fairness-aware Deep Multi-task Learning
We introduce the L2T-FMT algorithm that is a teacher-student network trained collaboratively; the student learns to solve the fair MTL problem while the teacher instructs the student to learn from either accuracy or fairness, depending on what is harder to learn for each task.
Parity-based Cumulative Fairness-aware Boosting
Data-driven AI systems can lead to discrimination on the basis of protected attributes like gender or race.
A survey on datasets for fairness-aware machine learning
For a deeper understanding of bias in the datasets, we investigate the interesting relationships using exploratory analysis.
Multi-fairness under class-imbalance
Recent studies showed that datasets used in fairness-aware machine learning for multiple protected attributes (referred to as multi-discrimination hereafter) are often imbalanced.
Fair-Capacitated Clustering
To this end, we introduce the fair-capacitated clustering problem that partitions the data into clusters of similar instances while ensuring cluster fairness and balancing cluster cardinalities.
Exploiting stance hierarchies for cost-sensitive stance detection of Web documents
In this context, stance detection aims at identifying the position (stance) of a document towards a claim.
A Deep Ensemble Framework for Fake News Detection and Classification
Fake news, rumor, incorrect information, and misinformation detection are nowadays crucial issues as these might have serious consequences for our social fabrics.
An Ensemble Approach for Aggression Identification in English and Hindi Text
This paper describes our system submitted in the shared task at COLING 2018 TRAC-1: Aggression Identification.
