90 research outputs found
Evaluating the Underlying Gender Bias in Contextualized Word Embeddings
Gender bias is highly impacting natural language processing applications.
Word embeddings have clearly been proven both to keep and amplify gender biases
that are present in current data sources. Recently, contextualized word
embeddings have enhanced previous word embedding techniques by computing word
vector representations dependent on the sentence they appear in.
In this paper, we study the impact of this conceptual change in the word
embedding computation in relation with gender bias. Our analysis includes
different measures previously applied in the literature to standard word
embeddings. Our findings suggest that contextualized word embeddings are less
biased than standard ones even when the latter are debiased
Towards Socially Responsible AI: Cognitive Bias-Aware Multi-Objective Learning
Human society had a long history of suffering from cognitive biases leading
to social prejudices and mass injustice. The prevalent existence of cognitive
biases in large volumes of historical data can pose a threat of being
manifested as unethical and seemingly inhuman predictions as outputs of AI
systems trained on such data. To alleviate this problem, we propose a
bias-aware multi-objective learning framework that given a set of identity
attributes (e.g. gender, ethnicity etc.) and a subset of sensitive categories
of the possible classes of prediction outputs, learns to reduce the frequency
of predicting certain combinations of them, e.g. predicting stereotypes such as
`most blacks use abusive language', or `fear is a virtue of women'. Our
experiments conducted on an emotion prediction task with balanced class priors
shows that a set of baseline bias-agnostic models exhibit cognitive biases with
respect to gender, such as women are prone to be afraid whereas men are more
prone to be angry. In contrast, our proposed bias-aware multi-objective
learning methodology is shown to reduce such biases in the predictied emotions
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