Social media accounts from the pro-Chinese political spam network Spamouflage Dragon started posting English-language videos that attacked American policy and the administration of U.S. President Donald Trump in June, as the rhetorical confrontation between the United States and China escalated.
The videos were clumsily made, marked by language errors and awkward automated voice-overs. Some of the accounts on YouTube and Twitter used AI-generated profile pictures, a technique that appears to be increasingly common in disinformation campaigns. The network did not appear to receive any engagement from authentic users across social media platforms, nor did it appear to seriously attempt to conceal its Chinese origin as it pivoted toward messaging related to U.S. politics. Read More
Daily Archives: August 13, 2020
Assessing Demographic Bias in Named Entity Recognition
Named Entity Recognition (NER) is often the first step towards automated Knowledge Base (KB) generation from raw text. In this work, we assess the bias in various Named Entity Recognition(NER) systems for English across different demographic groups with synthetically generated corpora. Our analysis reveals that models perform better at identifying names from specific demo-graphic groups across two datasets. We also identify that debiased embeddings do not help in resolving this issue. Finally, we observe that character-based contextualized word representation models such as ELMo results in the least bias across demographics. Our work can shed light on potential biases in automated KB generation due to systematic exclusion of named entities belonging to certain demographics. Read More
Characterizing Implicit Bias in Terms of Optimization Geometry
We study the implicit bias of generic optimization methods, such as mirror descent, natural gradient descent,and steepest descent with respect to different potentials and norms, when optimizing under determined linear regression or separable linear classification problems. We explore the question of whether the specific global minimum (among the many possible global minima) reached by an algorithm can be characterized in terms of the potential or norm of the optimization geometry, and independently of hyperparameter choices such as step-size and momentum. Read More
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