Search Results for « Gender Balance »

Modern models for common NLP tasks often employ machine learning techniques and train on journalistic, social media, or other culturally-derived text. These have recently been scrutinized for racial and gender biases, rooting from inherent bias in their training text. These biases are often sub-optimal and recent work poses methods to rectify them; however, these biases may shed light on actual racial or gender gaps in the culture(s) that produced the training text, thereby helping us understand cultural context through big data. This paper presents an approach for quantifying gender bias in word embeddings, and then using them to characterize statistical gender gaps in education, politics, economics, and health. We validate these metrics on 2018 Twitter data spanning 51 U.S. regions and 99 countries. We correlate state and country word embedding biases with 18 international and 5 U.S.-based statistical gender gaps, characterizing regularities and predictive strength.

Gender balance is defined as equal participation of women and men. Aiming for gender balance can be one pathway towards more equal, diverse, and inclusive societies. Gender balance can be achieved both vertically and horizontally within an organisation. Vertical gender balance is defined as an equal proportion of women and men in ranked positions of power. Horizontal gender balance is defined as an equal proportion of women and men across different fields of practices. A major limitation of the concept is that it builds on a binary understanding of gender which leads to statistical data handling that does not take gender diversity into account. Gender balance in academia needs to be seen in a broader context of transformational processes towards a more gender equal society. Innovative measures to map and monitor gender balance in research leadership have been recently developed in Norway. Those who work with the concept need to keep in mind that balanced representation does not necessarily lead to a redistribution of power.

We observe an instance of gender-induced bias in a downstream application, despite the absence of explicit gender words in the test cases. We provide a test set, SoWinoBias, for the purpose of measuring such latent gender bias in coreference resolution systems. We evaluate the performance of current debiasing methods on the SoWinoBias test set, especially in reference to the method's design and altered embedding space properties. See https://github.com/hillarydawkins/SoWinoBias.

Ethics regarding social bias has recently thrown striking issues in natural language processing. Especially for gender-related topics, the need for a system that reduces the model bias has grown in areas such as image captioning, content recommendation, and automated employment. However, detection and evaluation of gender bias in the machine translation systems are not yet thoroughly investigated, for the task being cross-lingual and challenging to define. In this paper, we propose a scheme for making up a test set that evaluates the gender bias in a machine translation system, with Korean, a language with gender-neutral pronouns. Three word/phrase sets are primarily constructed, each incorporating positive/negative expressions or occupations; all the terms are gender-independent or at least not biased to one side severely. Then, additional sentence lists are constructed concerning formality of the pronouns and politeness of the sentences. With the generated sentence set of size 4,236 in total, we evaluate gender bias in conventional machine translation systems utilizing the proposed measure, which is termed here as translation gender bias index (TGBI). The corpus and the code for evaluation is available on-line.

Abstract We find that forcing radical gender balance on corporate boards is associated with increased board independence and reduced firm value. A mandatory 40% gender quota shifts the average fraction of independent directors from 46% to 67% because female directors are much more often independent directors than males are. This shock to board independence via gender quotas is strongest in small, young, profitable, non‐listed firms with powerful stockholders and few female directors. Such firms also lose the most value, presumably because they need advice from dependent directors the most and monitoring by independent directors the least.

Neural Machine Translation (NMT) has been shown to struggle with grammatical gender that is dependent on the gender of human referents, which can cause gender bias effects. Many existing approaches to this problem seek to control gender inflection in the target language by explicitly or implicitly adding a gender feature to the source sentence, usually at the sentence level. In this paper we propose schemes for incorporating explicit word-level gender inflection tags into NMT. We explore the potential of this gender-inflection controlled translation when the gender feature can be determined from a human reference, or when a test sentence can be automatically gender-tagged, assessing on English-to-Spanish and English-to-German translation. We find that simple existing approaches can over-generalize a gender-feature to multiple entities in a sentence, and suggest effective alternatives in the form of tagged coreference adaptation data. We also propose an extension to assess translations of gender-neutral entities from English given a corresponding linguistic convention, such as a non-binary inflection, in the target language.

Hackathons are events that originated as competitions for programmers but transformed into project-oriented events given in different contexts and with different sets of goals. Despite the wide range of topics and business fields covered by current hackathons, a lot of events lack female participants. The lack of gender diversity is a problem, as diversity is a well-known and significant contributor to idea generation, innovativeness, and the efficiency of making novel development. This article covered the current literature, identifying the reasons why women show low interest in hackathon participation. Among these reasons, there is impostor syndrome caused by gender stereotypes, the difference between male and female experiences, for example in video games, as well as the undeserved bad reputation of hackathons as sleepless events, aimed only for specialists and guru programmers. However, hackathon organizers have found positive experiences of attracting more women to their events by implementing female-inclusive measures. Based on the literature, we have compiled recommendations to be considered and implemented in event organizing strategies, implementation models, and promotional approaches to achieve better gender balance at hackathon events.

Neural machine translation (NMT) models often suffer from gender biases that harm users and society at large. In this work, we explore how bridging the gap between languages for which parallel data is not available affects gender bias in multilingual NMT, specifically for zero-shot directions. We evaluate translation between grammatical gender languages which requires preserving the inherent gender information from the source in the target language. We study the effect of encouraging language-agnostic hidden representations on models' ability to preserve gender and compare pivot-based and zero-shot translation regarding the influence of the bridge language (participating in all language pairs during training) on gender preservation. We find that language-agnostic representations mitigate zero-shot models' masculine bias, and with increased levels of gender inflection in the bridge language, pivoting surpasses zero-shot translation regarding fairer gender preservation for speaker-related gender agreement.

This report has been produced by the Forum on Tax Administration (FTA) Gender Balance Network (GBN). It contains examples of initiatives, legislative and administrative, taken by a number of GBN members or their governments to support and enhance gender balance. It provides helpful comparative information, including for the consideration of possible future policies and activities at the domestic level. Work on this report was led within the GBN by officials from the Canada Revenue Agency (CRA). It is the second in an intended series of reports by the FTA’s Networks and Communities of Interest which bring together experts to exchange views and work collaboratively on major themes of modern tax administration.