Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

@inproceedings{Zhao2017MenAL,
  title={Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints},
  author={Jieyu Zhao and Tianlu Wang and Mark Yatskar and Vicente Ordonez and Kai-Wei Chang},
  booktitle={EMNLP},
  year={2017}
}
Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. [] Key Method 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…

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References

SHOWING 1-10 OF 40 REFERENCES

Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

TLDR
This work empirically demonstrates that its algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks.

Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels

TLDR
This paper proposes an algorithm to decouple the human reporting bias from the correct visually grounded labels, and shows significant improvements over traditional algorithms for both image classification and image captioning, doubling the performance of existing methods in some cases.

Semantics derived automatically from language corpora necessarily contain human biases

TLDR
It is shown for the first time that human-like semantic biases result from the application of standard machine learning to ordinary language---the same sort of language humans are exposed to every day.

Semantics derived automatically from language corpora contain human-like biases

TLDR
It is shown that machines can learn word associations from written texts and that these associations mirror those learned by humans, as measured by the Implicit Association Test (IAT), and that applying machine learning to ordinary human language results in human-like semantic biases.

Situation Recognition: Visual Semantic Role Labeling for Image Understanding

This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including: (1) the main activity (e.g., clipping), (2) the participating

Tractable Semi-supervised Learning of Complex Structured Prediction Models

TLDR
An approximate semi-supervised learning method that uses piecewise training for estimating the model weights and a dual decomposition approach for solving the inference problem of finding the labels of unlabeled data subject to domain specific constraints is proposed.

Constrained Semi-supervised Learning in the Presence of Unanticipated Classes

TLDR
This thesis argues that many AKBC tasks which have previously been addressed separately can be viewed as instances of single abstract problem: multiview semisupervised learning with an incomplete class hierarchy, and presents a generic EM framework for solving this abstract task.

VQA: Visual Question Answering

We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language

Extracting implicit knowledge from text

TLDR
This work considers the extraction of knowledge that is conveyed implicitly, both within everyday texts and queries posed to internet search engines, and shows that a significant amount of general knowledge can be gleaned based on how the authors talk about the world.

A survey on measuring indirect discrimination in machine learning

TLDR
This survey review and organize various discrimination measures that have been used for measuring discrimination in data, as well as in evaluating performance of discrimination-aware predictive models, and computationally analyze properties of selected measures.