1,175 research outputs found
Logic Against Bias: Textual Entailment Mitigates Stereotypical Sentence Reasoning
Due to their similarity-based learning objectives, pretrained sentence
encoders often internalize stereotypical assumptions that reflect the social
biases that exist within their training corpora. In this paper, we describe
several kinds of stereotypes concerning different communities that are present
in popular sentence representation models, including pretrained next sentence
prediction and contrastive sentence representation models. We compare such
models to textual entailment models that learn language logic for a variety of
downstream language understanding tasks. By comparing strong pretrained models
based on text similarity with textual entailment learning, we conclude that the
explicit logic learning with textual entailment can significantly reduce bias
and improve the recognition of social communities, without an explicit
de-biasing processComment: Accepted by EACL 202
Gender-tuning: Empowering Fine-tuning for Debiasing Pre-trained Language Models
Recent studies have revealed that the widely-used Pre-trained Language Models
(PLMs) propagate societal biases from the large unmoderated pre-training
corpora. Existing solutions require debiasing training processes and datasets
for debiasing, which are resource-intensive and costly. Furthermore, these
methods hurt the PLMs' performance on downstream tasks. In this study, we
propose Gender-tuning, which debiases the PLMs through fine-tuning on
downstream tasks' datasets. For this aim, Gender-tuning integrates Masked
Language Modeling (MLM) training objectives into fine-tuning's training
process. Comprehensive experiments show that Gender-tuning outperforms the
state-of-the-art baselines in terms of average gender bias scores in PLMs while
improving PLMs' performance on downstream tasks solely using the downstream
tasks' dataset. Also, Gender-tuning is a deployable debiasing tool for any PLM
that works with original fine-tuning
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
Survey of Social Bias in Vision-Language Models
In recent years, the rapid advancement of machine learning (ML) models,
particularly transformer-based pre-trained models, has revolutionized Natural
Language Processing (NLP) and Computer Vision (CV) fields. However, researchers
have discovered that these models can inadvertently capture and reinforce
social biases present in their training datasets, leading to potential social
harms, such as uneven resource allocation and unfair representation of specific
social groups. Addressing these biases and ensuring fairness in artificial
intelligence (AI) systems has become a critical concern in the ML community.
The recent introduction of pre-trained vision-and-language (VL) models in the
emerging multimodal field demands attention to the potential social biases
present in these models as well. Although VL models are susceptible to social
bias, there is a limited understanding compared to the extensive discussions on
bias in NLP and CV. This survey aims to provide researchers with a high-level
insight into the similarities and differences of social bias studies in
pre-trained models across NLP, CV, and VL. By examining these perspectives, the
survey aims to offer valuable guidelines on how to approach and mitigate social
bias in both unimodal and multimodal settings. The findings and recommendations
presented here can benefit the ML community, fostering the development of
fairer and non-biased AI models in various applications and research endeavors
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