80,190 research outputs found
On Measuring Bias in Online Information
Bias in online information has recently become a pressing issue, with search
engines, social networks and recommendation services being accused of
exhibiting some form of bias. In this vision paper, we make the case for a
systematic approach towards measuring bias. To this end, we discuss formal
measures for quantifying the various types of bias, we outline the system
components necessary for realizing them, and we highlight the related research
challenges and open problems.Comment: 6 pages, 1 figur
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Language is increasingly being used to define rich visual recognition
problems with supporting image collections sourced from the web. Structured
prediction models are used in these tasks to take advantage of correlations
between co-occurring labels and visual input but risk inadvertently encoding
social biases found in web corpora. In this work, we study data and models
associated with multilabel object classification and visual semantic role
labeling. We find that (a) datasets for these tasks contain significant gender
bias and (b) models trained on these datasets further amplify existing bias.
For example, the activity cooking is over 33% more likely to involve females
than males in a training set, and a trained model further amplifies the
disparity to 68% at test time. We propose to inject corpus-level constraints
for calibrating existing structured prediction models and design an algorithm
based on Lagrangian relaxation for collective inference. Our method results in
almost no performance loss for the underlying recognition task but decreases
the magnitude of bias amplification by 47.5% and 40.5% for multilabel
classification and visual semantic role labeling, respectively.Comment: 11 pages, published in EMNLP 201
The Cinderella Complex: Word Embeddings Reveal Gender Stereotypes in Movies and Books
Our analysis of thousands of movies and books reveals how these cultural
products weave stereotypical gender roles into morality tales and perpetuate
gender inequality through storytelling. Using the word embedding techniques, we
reveal the constructed emotional dependency of female characters on male
characters in stories
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
Transforming Perception: Black Men and Boys
While there has been progress in the U.S. in terms of racial attitudes and opportunities, black men and boys continue to face challenges. This report presents original research, along with current studies in social psychology and neuroscience, offering an empirically grounded analysis of how emotions and fears about race shape behaviors and biases
MEDIA EFFECTS ON THE NEW YORK TIMES’ “THE WOMEN’S MARCH IN WASHINGTON” VIDEO NEWS COVERAGE ON FACEBOOK
The reliance towards Facebook in regard to obtaining information becomes a news habit among the society. Considerable number of news coverage from media is accessible to Facebook which creates effects on the audience on account of the media exposure. The study is conducted for the purposes of analyzing news elements which are embedded in The New York Times' “The Women's March in Wahsington”video news coverage on Facebook and discovering the effects of the coverage towards media audience. This study is constructed as a library research which utilizes textual and user-response analysis research methodology. The theory utilizes to support the study is Pan &Kosicki's Framing Analysis, and McComb& Shaw's Agenda-Setting theory is also applied in this study to support the framing analysis. The results of the study indicate that three salient elements of the coverage set public agenda to which the salient elements become prominent issues of the Women's March on Washington
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