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Exacerbating Algorithmic Bias through Fairness Attacks
Ninareh Mehrabi, Muhammad Naveed, Fred Morstatter +1
Algorithmic fairness has attracted significant attention in recent years, with many quantitative measures suggested for characterizing the fairness of different machine learning al…
Political Partisanship and Anti-Science Attitudes in Online Discussions about Covid-19
Ashwin Rao, Fred Morstatter, Minda Hu +4
The novel coronavirus pandemic continues to ravage communities across the US. Opinion surveys identified importance of political ideology in shaping perceptions of the pandemic and…
One-shot Learning for Temporal Knowledge Graphs
Mehrnoosh Mirtaheri, Mohammad Rostami, Xiang Ren +2
Most real-world knowledge graphs are characterized by a long-tail relation frequency distribution where a significant fraction of relations occurs only a handful of times. This obs…
Leveraging Clickstream Trajectories to Reveal Low-Quality Workers in Crowdsourced Forecasting Platforms
Akira Matsui, Emilio Ferrara, Fred Morstatter +2
Crowdwork often entails tackling cognitively-demanding and time-consuming tasks. Crowdsourcing can be used for complex annotation tasks, from medical imaging to geospatial data, an…
Statistical Equity: A Fairness Classification Objective
Ninareh Mehrabi, Yuzhong Huang, Fred Morstatter
Machine learning systems have been shown to propagate the societal errors of the past. In light of this, a wealth of research focuses on designing solutions that are "fair." Even w…
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data
Woojeong Jin, Rahul Khanna, Suji Kim +4
Event forecasting is a challenging, yet important task, as humans seek to constantly plan for the future. Existing automated forecasting studies rely mostly on structured data, suc…