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Not all important information
are in tables.
Make use of what is in the texts and graphics of your reports. Contact us!
Contact Us
Not all important information
are in tables.
Make use of what is in the texts and graphics of your reports. Contact us!
Briefly noted: Gender Bias Occupies a Linear Subspace
Gender bias is the most studied fairness issue in Natural Language Processing. In their recent EMNLP-2020 article, Vargas & Cotterell show that within word embedding space, gender bias occupies a linear subspace.
Up-and-coming: Fairness in Natural Language Processing
Fairness is a central aspect of the ethically responsible development and application of Artificial Intelligence. Because humans may be biased, so may be Machine Learning (ML) models trained on data that reflects human biases.
Briefly noted: Backdoors in Federated Learning
In their NeurIPS-2020 article, Wang et al. discuss the injection of backdoors into a model during model training. In an FL setting, the goal of a backdoor is to corrupt the global (federated) model such that it mispredicts on a targeted sub-task.
