112,133 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
Evaluating the role of quantitative modeling in language evolution
Models are a flourishing and indispensable area of research in language evolution. Here we highlight critical issues in using and interpreting models, and suggest viable approaches. First, contrasting models can explain the same data and similar modelling techniques can lead to diverging conclusions. This should act as a reminder to use the extreme malleability of modelling parsimoniously when interpreting results. Second, quantitative techniques similar to those used in modelling language evolution have proven themselves inadequate in other disciplines. Cross-disciplinary fertilization is crucial to avoid mistakes which have previously occurred in other areas. Finally, experimental validation is necessary both to sharpen models' hypotheses, and to support their conclusions. Our belief is that models should be interpreted as quantitative demonstrations of logical possibilities, rather than as direct sources of evidence. Only an integration of theoretical principles, quantitative proofs and empirical validation can allow research in the evolution of language to progress
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
Ask Your Neurons: A Neural-based Approach to Answering Questions about Images
We address a question answering task on real-world images that is set up as a
Visual Turing Test. By combining latest advances in image representation and
natural language processing, we propose Neural-Image-QA, an end-to-end
formulation to this problem for which all parts are trained jointly. In
contrast to previous efforts, we are facing a multi-modal problem where the
language output (answer) is conditioned on visual and natural language input
(image and question). Our approach Neural-Image-QA doubles the performance of
the previous best approach on this problem. We provide additional insights into
the problem by analyzing how much information is contained only in the language
part for which we provide a new human baseline. To study human consensus, which
is related to the ambiguities inherent in this challenging task, we propose two
novel metrics and collect additional answers which extends the original DAQUAR
dataset to DAQUAR-Consensus.Comment: ICCV'15 (Oral
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