1 citations · 2 across the 8 of their papers we have counts for
9 papers
Mitigating Demographic Disparities in Explanation-Human Alignment via Rationale Regularization
Suryansh Talukdar, Taha Sarwat, Jonathan Rusert
We study explanation fairness in hate speech detection. High classification performance does not always mean that a model produces equally plausible explanations for different demo…
Overcoming Black-box Attack Inefficiency with Hybrid and Dynamic Select Algorithms
Abhinay Shankar Belde, Rohit Ramkumar, Jonathan Rusert
Adversarial text attack research plays a crucial role in evaluating the robustness of NLP models. However, the increasing complexity of transformer-based architectures has dramatic…
RedHerring Attack: Testing the Reliability of Attack Detection
Jonathan Rusert
In response to adversarial text attacks, attack detection models have been proposed and shown to successfully identify text modified by adversaries. Attack detection models can be…
BinarySelect to Improve Accessibility of Black-Box Attack Research
Shatarupa Ghosh, Jonathan Rusert
Adversarial text attack research is useful for testing the robustness of NLP models, however, the rise of transformers has greatly increased the time required to test attacks. Espe…
VertAttack: Taking advantage of Text Classifiers' horizontal vision
Jonathan Rusert
Text classification systems have continuously improved in performance over the years. However, nearly all current SOTA classifiers have a similar shortcoming, they process text in…
Don't sweat the small stuff, classify the rest: Sample Shielding to protect text classifiers against adversarial attacks
Jonathan Rusert, Padmini Srinivasan
Deep learning (DL) is being used extensively for text classification. However, researchers have demonstrated the vulnerability of such classifiers to adversarial attacks. Attackers…
A Girl Has A Name, And It's ... Adversarial Authorship Attribution for Deobfuscation
Wanyue Zhai, Jonathan Rusert, Zubair Shafiq +1
Recent advances in natural language processing have enabled powerful privacy-invasive authorship attribution. To counter authorship attribution, researchers have proposed a variety…
Suum Cuique: Studying Bias in Taboo Detection with a Community Perspective
Osama Khalid, Jonathan Rusert, Padmini Srinivasan
Prior research has discussed and illustrated the need to consider linguistic norms at the community level when studying taboo (hateful/offensive/toxic etc.) language. However, a me…
On The Robustness of Offensive Language Classifiers
Jonathan Rusert, Zubair Shafiq, Padmini Srinivasan
Social media platforms are deploying machine learning based offensive language classification systems to combat hateful, racist, and other forms of offensive speech at scale. Howev…