435 research outputs found
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
Demographic Inference and Representative Population Estimates from Multilingual Social Media Data
Social media provide access to behavioural data at an unprecedented scale and
granularity. However, using these data to understand phenomena in a broader
population is difficult due to their non-representativeness and the bias of
statistical inference tools towards dominant languages and groups. While
demographic attribute inference could be used to mitigate such bias, current
techniques are almost entirely monolingual and fail to work in a global
environment. We address these challenges by combining multilingual demographic
inference with post-stratification to create a more representative population
sample. To learn demographic attributes, we create a new multimodal deep neural
architecture for joint classification of age, gender, and organization-status
of social media users that operates in 32 languages. This method substantially
outperforms current state of the art while also reducing algorithmic bias. To
correct for sampling biases, we propose fully interpretable multilevel
regression methods that estimate inclusion probabilities from inferred joint
population counts and ground-truth population counts. In a large experiment
over multilingual heterogeneous European regions, we show that our demographic
inference and bias correction together allow for more accurate estimates of
populations and make a significant step towards representative social sensing
in downstream applications with multilingual social media.Comment: 12 pages, 10 figures, Proceedings of the 2019 World Wide Web
Conference (WWW '19
Language (Technology) is Power: A Critical Survey of "Bias" in NLP
We survey 146 papers analyzing "bias" in NLP systems, finding that their
motivations are often vague, inconsistent, and lacking in normative reasoning,
despite the fact that analyzing "bias" is an inherently normative process. We
further find that these papers' proposed quantitative techniques for measuring
or mitigating "bias" are poorly matched to their motivations and do not engage
with the relevant literature outside of NLP. Based on these findings, we
describe the beginnings of a path forward by proposing three recommendations
that should guide work analyzing "bias" in NLP systems. These recommendations
rest on a greater recognition of the relationships between language and social
hierarchies, encouraging researchers and practitioners to articulate their
conceptualizations of "bias"---i.e., what kinds of system behaviors are
harmful, in what ways, to whom, and why, as well as the normative reasoning
underlying these statements---and to center work around the lived experiences
of members of communities affected by NLP systems, while interrogating and
reimagining the power relations between technologists and such communities
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
Exploring Gender Bias in Semantic Representations for Occupational Classification in NLP: Techniques and Mitigation Strategies
Gender bias in Natural Language Processing (NLP) models is a non-trivial problem that can perpetuate and amplify existing societal biases. This thesis investigates gender bias in occupation classification and explores the effectiveness of different debiasing methods for language models to reduce the impact of bias in the model’s representations. The study employs a data-driven empirical methodology focusing heavily on experimentation and result investigation. The study uses five distinct semantic representations and models with varying levels of complexity to classify the occupation of individuals based on their biographies
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