132 research outputs found
RECAST: Interactive Auditing of Automatic Toxicity Detection Models
As toxic language becomes nearly pervasive online, there has been increasing
interest in leveraging the advancements in natural language processing (NLP),
from very large transformer models to automatically detecting and removing
toxic comments. Despite the fairness concerns, lack of adversarial robustness,
and limited prediction explainability for deep learning systems, there is
currently little work for auditing these systems and understanding how they
work for both developers and users. We present our ongoing work, RECAST, an
interactive tool for examining toxicity detection models by visualizing
explanations for predictions and providing alternative wordings for detected
toxic speech.Comment: 8 Pages, 3 figures, The eighth International Workshop of Chinese CHI
Proceeding
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
Detecting East Asian Prejudice on Social Media
The outbreak of COVID-19 has transformed societies across the world as
governments tackle the health, economic and social costs of the pandemic. It
has also raised concerns about the spread of hateful language and prejudice
online, especially hostility directed against East Asia. In this paper we
report on the creation of a classifier that detects and categorizes social
media posts from Twitter into four classes: Hostility against East Asia,
Criticism of East Asia, Meta-discussions of East Asian prejudice and a neutral
class. The classifier achieves an F1 score of 0.83 across all four classes. We
provide our final model (coded in Python), as well as a new 20,000 tweet
training dataset used to make the classifier, two analyses of hashtags
associated with East Asian prejudice and the annotation codebook. The
classifier can be implemented by other researchers, assisting with both online
content moderation processes and further research into the dynamics, prevalence
and impact of East Asian prejudice online during this global pandemic.Comment: 12 page
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