Author name cluster
Faraaz Mallick
Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.
Possible papers
3AAMAS Conference 2022 Conference Paper
How Hard is Safe Bribery?
- Neel Karia
- Faraaz Mallick
- Palash Dey
Bribery in an election is one of the well-studied control problems in computational social choice. In this paper, we propose and study the safe bribery problem. Here the goal of the briber is to ask the bribed voters to vote in such a way that the briber never prefers the original winner (of the unbribed election) more than the new winner, even if the bribed voters do not fully follow the briber’s advice. Indeed, in many applications of bribery, campaigning for example, the briber often has limited control on whether the bribed voters eventually follow her recommendation and thus it is conceivable that the bribed voters can either partially or fully ignore the briber’s recommendation. We provide a comprehensive complexity theoretic landscape of the safe bribery problem for many common voting rules in this paper.
AAAI Conference 2022 Short Paper
INDEPROP: Information-Preserving De-propagandization of News Articles (Student Abstract)
- Aaryan Bhagat
- Faraaz Mallick
- Neel Karia
- Ayush Kaushal
We propose INDEPROP, a novel Natural Language Processing (NLP) application for combating online disinformation by mitigating propaganda from news articles. IN- DEPROP (Information-Preserving De-propagandization) involves fine-grained propaganda detection and its removal while maintaining document level coherence, grammatical correctness and most importantly, preserving the news articles’ information content. We curate the first large-scale dataset of its kind consisting of around 1M tokens. We also propose a set of automatic evaluation metrics for the same and observe its high correlation with human judgment. Furthermore, we show that fine-tuning the existing propaganda detection systems on our dataset considerably improves their generalization to the test set.