Guðbjörg Linda Rafnsdóttir

Artificial intelligence (AI) is increasingly reshaping critical areas such as hiring and workplace management, yet it risks systematically reproducing and amplifying existing inequalities, particularly regarding vulnerable groups. As addressing this urgent concern, we apply a feminist framework to examine gender differences in experiences of AI‐driven discrimination among employees and job seekers. Using survey data from 4417 individuals across 28 European countries, we demonstrate how such discrimination is patterned along gendered lines. Our findings suggest that women, older individuals, non‐White individuals, and those in non‐permanent employment are, overall, more likely than other participants to perceive or experience negative discrimination from AI applications. This pattern also extends to individuals identifying as “other gender.” Understanding intersecting inequalities and engaging feminist perspectives that question whose knowledge, values, and experiences are embedded in technological systems are essential. Only then can AI contribute to workplaces that are genuinely trustworthy. This, however, remains challenging given how ingrained inequality is in AI tools.

The period from 2003-2008 was one of upheaval and change in East Iceland with the single largest construction project in Iceland to date; the building of Kárahnjúkar dam and the Fjarðaál Alcoa aluminium smelter. These gave way to various societal changes in the area, such as population increase, new jobs and rise in housing prices, in addition to a range of side effects on the service factor. The aim of this article is to explore if women in the area describe new employment opportunities accompanying these changes. The results are derived from in-depth interviews with 34 women in East Iceland. The interviews unfold the view that a certain ‘modernization’ of the east Icelandic society can be attributed to the smelter construction. However, the women interviewed have difficulty defining whether and how they themselves had profited from the constructions and none of those interviewed in this research named working in the smelter as one of their employment opportunities. Among the reasons discussed were the twelve-hour shifts, lack of flexibilities, long distances and the binding to the workplace during work-hours. Results indicate that an unequal gender balance still remainsin the area.

The percentage of female doctorate holders has steadily increased in the Western world. Despite this trend, there is a scarcity of studies addressing gender disparities among doctorate holders, within and outside academia. We analyze the earnings of Icelandic doctorate holders either in the fields of science, technology, engineering, and mathematics (STEM) or social sciences and humanities (SSH) and are employed within and outside academia. We contend that achieving gender-wage equality is crucial for doctorate holders, given that education worldwide is considered a means to empower women and a significant factor in reducing gender inequality and narrowing the pay gap. Our findings reveal a continuous gender gap in total earnings between the fifth and tenth career years regardless of the field of study or the employment being within or outside academia. Thus, our findings suggest that a Ph.D. does not improve women’s earning power enough to close the gender pay gap.

We present an analysis of the representation of gender as a data dimension in data visualizations and propose a set of considerations around visual variables and annotations for gender-related data. Gender is a common demographic dimension of data collected from study or survey participants, passengers, or customers, as well as across academic studies, especially in certain disciplines like sociology. Our work contributes to multiple ongoing discussions on the ethical implications of data visualizations. By choosing specifc data, visual variables, and text labels, visualization designers may, inadvertently or not, perpetuate stereotypes and biases. Here, our goal is to start an evolving discussion on how to represent data on gender in data visualizations and raise awareness of the subtleties of choosing visual variables and words in gender visualizations. In order to ground this discussion, we collected and coded gender visualizations and their captions from fve different scientifc communities (Biology, Politics, Social Studies, Visualisation, and Human-Computer Interaction), in addition to images from Tableau Public and the Information Is Beautiful awards showcase. Overall we found that representation types are community-specifc, color hue is the dominant visual channel for gender data, and nonconforming gender is under-represented. We end our paper with a discussion of considerations for gender visualization derived from our coding and the literature and recommendations for large data collection bodies. A free copy of this paper and all supplemental materials are available at https://osf.io/v9ams/ .

We present an analysis of the representation of gender as a data dimension in data visualizations and propose a set of considerations around visual variables and annotations for gender-related data. Gender is a common demographic dimension of data collected from study or survey participants, passengers, or customers, as well as across academic studies, especially in certain disciplines like sociology. Our work contributes to multiple ongoing discussions on the ethical implications of data visualizations. By choosing specific data, visual variables, and text labels, visualization designers may, inadvertently or not, perpetuate stereotypes and biases. Here, our goal is to start an evolving discussion on how to represent data on gender in data visualizations and raise awareness of the subtleties of choosing visual variables and words in gender visualizations. In order to ground this discussion, we collected and coded gender visualizations and their captions from five different scientific communities (Biology, Politics, Social Studies, Visualisation, and Human-Computer Interaction), in addition to images from Tableau Public and the Information Is Beautiful awards showcase. Overall we found that representation types are community-specific, color hue is the dominant visual channel for gender data, and nonconforming gender is under-represented. We end our paper with a discussion of considerations for gender visualization derived from our coding and the literature and recommendations for large data collection bodies. A free copy of this paper and all supplemental materials are available at https://osf.io/v9ams/

This study examines the self-rated health and well-being of Icelandic teachers just before and over a year after COVID-19 first appeared. We ask, what was the stress level in 2021 compared to 2019 and the impact of mental and physical health and health symptoms on perceived stress? Were there any changes in self-assessed mental and physical health? Were there any changes in self-assessed mental and physical health symptoms? The study is based on an online survey conducted in 2019 and 2021. A total of 920 primary school teachers answered the questionnaire in part or in full, after three reminders. The main findings show increased stress, worsening mental and physical health, and increasing mental and physical symptoms in 2021 compared to 2019. The results also show a higher percentage of women than men reporting high stress, with women scoring higher on the PSS scale, but the gender patterns for mental and physical health are less clear. The results show that the COVID-19 pandemic had negative consequences on the health and well-being of the teachers. The study demonstrates the importance of school authorities keeping an exceptionally watchful eye on the welfare and well-being of teachers in the wake of the COVID-19 pandemic.

Research on gender equality in Higher Education indicates that female academics might not have the same opportunities for promotion as male academics. One of the are as in Higher Education where gender bias has been reported is in Student Evaluations of Teaching (SET). The aim of this article is to analyse possible gendered characteristics of communication between students and teachers in higher education, as reflected in SET. The article builds on mixed-method research. Five years of SET responses were analysed quantitatively and one year of open answers was analysed qualitatively. The qualitative data was then coded quantitatively. Iceland offers an excellent case for this study as it has been a forerunner in gender equality for years. The quantitative analysis shows that male students rate female teachers lower than their male counterparts, and the qualitative analyses indicate that the evaluation pattern of male and female teachers differs. Additionally, differences were found in the comments received by the teachers. Comments on male teachers referred to subject knowledge, while female teachers received comments on their service to students and relatability. There thus seems to be a gender bias in SET in Iceland despite its reputation for gender equality.

Gender disparities in top-level academic positions are persistent. However, whether bias in recruitment plays a role in producing these disparities remains unclear. This study examines the role of bias in academic recruitment by conducting a large-scale survey experiment among faculty in Economics, Law, Physics, Political Science, Psychology, and Sociology from universities in Iceland, Norway, and Sweden. The faculty respondents rated CVs of hypothetical candidates—who were randomly assigned either a male or a female name—for a permanent position as an Associate Professor in their discipline. The results show that, despite the underrepresentation of women in all fields, the female candidates were viewed as both more competent and more hireable compared to their male counterparts. Having children or a stronger CV do not change the overall result. Consequently, biased evaluations of equally qualified candidates to Associate Professor positions do not seem to be the key explanation of the persistent gender gap in academia in the Nordic region.

Research shows that bullying is a significant workplace issue. A previous study showed increased sickness-related absences among municipality employees during the Icelandic economic crisis in 2008. This led to the following research questions: has bullying and/or harassment increased between the time points of the study up to seven years after the crisis? Did bullying and/or harassment change depending on downsizing? Are quantitative job demands, role conflicts and social support connected to bullying and/or harassment at work and if so, how? The study is based on a four-wave longitudinal balanced panel dataset consisting of those who work within the education and care services operated by Icelandic municipalities. It was seen that bullying and harassment had increased between the time points of the study. Furthermore, employees in downsized workplaces, workplaces with higher quantitative job demands, more role conflicts and less support were more likely to experience bullying and/or harassment than employees in other workplaces. Since the effects may prevail for several years, the study demonstrates that the consequences of downsizing need to be carefully considered and that managers must be supported in that role. As economic crises tend to occur periodically, presently due to COVID-19, the knowledge is both of theoretical and practical importance.