Morstatter, Fred

A Survey on Bias and Fairness in Machine Learning

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2019) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 5,812

A Survey on Bias and Fairness in Machine Learning

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 5,812

Feature Selection: A Data Perspective

Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2018) · ACM Computing Surveys

Statistical Methods and InferenceFace and Expression Recognition

Cited by 2,442

Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose

Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. (2021) · Proceedings of the International AAAI Conference on Web and Social Media

Computational and Text Analysis MethodsComplex Network Analysis Techniques

Cited by 863

Tampering with Twitter’s Sample API

Pfeffer, J., Mayer, K., & Morstatter, F. (2018) · EPJ Data Science

Big Data Technologies and ApplicationsEthics and Social Impacts of AI

Cited by 135

When is it biased?: assessing the representativeness of twitter's streaming API

Morstatter, F., Pfeffer, J., & Liu, H. (2014) · Proceedings of the 23rd International Conference on World Wide Web

Ethics and Social Impacts of AISentiment Analysis and Opinion Mining

Cited by 90

The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance

Salinas, A., & Morstatter, F. (2024) · arXiv

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 49

The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance

Salinas, A., & Morstatter, F. (2024) · Findings of the Association for Computational Linguistics ACL 2024

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 48

Can One Tamper with the Sample API?: Toward Neutralizing Bias from Spam and Bot Content

Morstatter, F., Dani, H., Sampson, J., & Liu, H. (2016) · Proceedings of the 25th International Conference Companion on World Wide Web - WWW 16 Companion

Spam and Phishing DetectionMisinformation and Its Impacts

Cited by 24

Quantifying machine influence over human forecasters

Abeliuk, A., Benjamin, D. M., Morstatter, F., & Galstyan, A. (2020) · Scientific Reports

Forecasting Techniques and ApplicationsHuman-Automation Interaction and Safety

Cited by 18