A little bird told me your gender: Gender inferences in social media

Fosch‐Villaronga, Eduard; Poulsen, Adam; Søraa, Roger Andre; Custers, Bart

doi:10.1016/j.ipm.2021.102541

Cited by 56 publications

(31 citation statements)

References 37 publications

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“…Gender bias can be defined as a systematic preference or discrimination against people of a particular gender (Friedman and Nissenbaum, 1996). NLP systems that exhibit such biased behavior can perform better for the favored gender, e.g., speech recognizers that achieve higher accuracy for male voices (Tatman, 2017), or reinforce harmful stereotypes, e.g., social media platforms that misgender LGBT+ community members (Villaronga et al, 2021). 1 Biased behaviors have (in-)direct origins in statistical patterns occurring in data that the NLP models are trained on (Sun et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Assessing Gender Bias in Wikipedia: Inequalities in Article Titles

Faleńska¹,

Çetinoğlu²

2021

Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing

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Potential gender biases existing in Wikipedia's content can contribute to biased behaviors in a variety of downstream NLP systems. Yet, efforts in understanding what inequalities in portraying women and men occur in Wikipedia focused so far only on biographies, leaving open the question of how often such harmful patterns occur in other topics. In this paper, we investigate gender-related asymmetries in Wikipedia titles from all domains. We assess that for only half of gender-related articles, i.e., articles with words such as women or male in their titles, symmetrical counterparts describing the same concept for the other gender (and clearly stating it in their titles) exist. Among the remaining imbalanced cases, the vast majority of articles concern sports-and social-related issues. We provide insights on how such asymmetries can influence other Wikipedia components and propose steps towards reducing the frequency of observed patterns.Bias statement Inequalities in how men and women are represented in Wikipedia titles can be captured by NLP models and translate into biased behaviors creating representational harms (Blodgett et al., 2020). For example, if by default Wikipedia articles about national sports teams are about male teams, then a search engine might assume that a prototypical sportsperson is a man. Such a system when asked about famous volleyball players might exhibit recognition bias and return no women. Similarly, if Wikipedia provides special articles listing women photographers next to photographers, then an automatic knowledge extractor trained on such data might learn and propagate stereotypical generalizations that women within these occupations are an exception and should hold special qualities. Our work limits itself to binary gender values in extracting gender bias patterns from Wikipedia titles due to data scarcity. We acknow-ledge that not incorporating other values into our analysis indirectly causes recognition bias against non-binary people.

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Section: Introductionmentioning

confidence: 99%

Assessing Gender Bias in Wikipedia: Inequalities in Article Titles

Faleńska¹,

Çetinoğlu²

2021

Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing

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“…Technology ethics is not a new research domain. It has been studied in different contexts, for example, online communities [ 30 ], ethics education [ 34 ], gender and tech [ 24 , 36 ]. Similarly, in HRI/CRI, researchers have reviewed various example settings where ethical issues arise and proposed specific principles that one should consider as an HRI/CRI practitioner [ 61 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Ethical Considerations from Child-Robot Interactions in Under-Resourced Communities

Singh

Kumar

Fosch‐Villaronga

et al. 2022

Int J of Soc Robotics

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Recent advancements in socially assistive robotics (SAR) have shown a significant potential of using social robotics to achieve increasing cognitive and affective outcomes in education. However, the deployments of SAR technologies also bring ethical challenges in tandem, to the fore, especially in under-resourced contexts. While previous research has highlighted various ethical challenges that arise in SAR deployment in real-world settings, most of the research has been centered in resource-rich contexts, mainly in developed countries in the ‘Global North,’ and the work specifically in the educational setting is limited. This research aims to evaluate and reflect upon the potential ethical and pedagogical challenges of deploying a social robot in an under-resourced context. We base our findings on a 5-week in-the-wild user study conducted with 12 kindergarten students at an under-resourced community school in New Delhi, India. We used interaction analysis with the context of learning, education, and ethics to analyze the user study through video recordings. Our findings highlighted four primary ethical considerations that should be taken into account while deploying social robotics technologies in educational settings; (1) language and accent as barriers in pedagogy, (2) effect of malfunctioning, (un)intended harms, (3) trust and deception, and (4) ecological viability of innovation. Overall, our paper argues for assessing the ethical and pedagogical constraints and bridging the gap between non-existent literature from such a context to evaluate better the potential use of such technologies in under-resourced contexts.

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“…How demographic data is coded and represented in datasets -specifically, what categories are being used to define individual characteristics -can have a significant impact on the representation of marginalized individuals. When ADMS fail to accurately determine an individual's identity, such miscategorization and identity misrepresentation may not only lead to social and political discrimination, but also psychological and emotional harms via feelings of invalidation and rejection [39].…”

Section: Miscategorization and Identity Misrepresentationmentioning

confidence: 99%

Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness

Andrus¹,

Villeneuve²

2022

Preprint

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Most proposed algorithmic fairness techniques require access to demographic data in order to make performance comparisons and standardizations across groups, however this data is largely unavailable in practice, hindering the widespread adoption of algorithmic fairness. Through this paper, we consider calls to collect more data on demographics to enable algorithmic fairness and challenge the notion that discrimination can be overcome with smart enough technical methods and sufficient data. We show how these techniques largely ignore broader questions of data governance and systemic oppression when categorizing individuals for the purpose of fairer algorithmic processing. In this work, we explore under what conditions demographic data should be collected and used to enable algorithmic fairness methods by characterizing a range of social risks to individuals and communities. For the risks to individuals we consider the unique privacy risks of sensitive attributes, the possible harms of miscategorization and misrepresentation, and the use of sensitive data beyond data subjects' expectations. Looking more broadly, the risks to entire groups and communities include the expansion of surveillance infrastructure in the name of fairness, misrepresenting and mischaracterizing what it means to be part of a demographic group, and ceding the ability to define what constitutes biased or unfair treatment. We argue that, by confronting these questions before and during the collection of demographic data, algorithmic fairness methods are more likely to actually mitigate harmful treatment disparities without reinforcing systems of oppression. Towards this end, we assess privacy-focused methods of data collection and use and participatory data governance structures as proposals for more responsibly collecting demographic data.CCS Concepts: • Security and privacy → Social aspects of security and privacy; Privacy protections; Economics of security and privacy; • Social and professional topics → User characteristics; Gender; Sexual orientation.

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A little bird told me your gender: Gender inferences in social media

Cited by 56 publications

References 37 publications

Assessing Gender Bias in Wikipedia: Inequalities in Article Titles

Assessing Gender Bias in Wikipedia: Inequalities in Article Titles

Ethical Considerations from Child-Robot Interactions in Under-Resourced Communities

Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness

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