I am happy to say that our paper was accepted at #neurips2022 and we released not 1, not 2, but 6 companion datasets!🎉

The paper, "Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation" presents a suite of datasets to evaluate ML fairness.😎

We are releasing these datasets because the normally currently used datasets to test ML fairness are small (less than 50k rows), are old (e.g., the frequently used "UCI Adult" is from 1994), or have documentation issues or data leakage.

On the contrary, our datasets are realistic, large (1 million rows), contain 5 different types of biases, have real-world properties (imbalanced, time shifts), and are privacy-preserving (via GANs, Laplacian noise, and filters and transformations).

Paper, datasets, datasheets, and code are available at:
https://lnkd.in/ddDm4Y64

Thank you co-authors Sérgio Jesus, José Maria Pombal, Duarte Alves, André Cruz, Pedro Saleiro, Rita P. Ribeiro, and Joao Gama, in another collaboration between Feedzai and Universidade do Porto.

Thank you also to colleagues and helping friends João Veiga, Joao Bravo, Catarina Belém, and Bruno Cabral and to sponsoring agency ANI - Agência Nacional de Inovação and Carnegie Mellon Portugal Program.

#feedzai
#frauddetection
#ResponsibleAI
#MLfairness

I constantly say #rightwingcontentisspam but realize I should explain:

I'm not trying to throw shade, rather, objectively, I am shining a light on the ecosystem that has captured right leaning audience attention.

Pretend this is a comparison matrix for [Spam , Right wing content]:

Deceptive content: [✅|,✅]
Manipulates emotions to get you to click: [✅,✅]
Shameless ads and monetization: [✅,✅]
Low quality info: [✅,✅]
Filtered by most platforms [✅,❌ ]

#mlfairness #spam #propaganda #fakenews