Browsing by Author "Kaltenhäuser, Kristin"

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    Conference Paper
    CSCW and Algorithmic Systems
    (Proceedings of 20th European Conference on Computer-Supported Cooperative Work, 2022) Lampinen, Airi; Møller, Naja Holten; Sheikh, Riyaz; Ammitzbøll Flügge, Asbjørn; Kaltenhäuser, Kristin; Cakici, Baki
    The European Union announced recently that Europe should be a global hub and leader in the development of Artifcial Intelligence (AI) that guarantees safety and fundamental rights (European Commission (2021)). In this workshop, we investigate how we can approach this challenge from the perspective of Computer-Supported Cooperative Work (CSCW). Starting with a general conceptual focus on algorithmic systems and their increasing role in society, we are particularly interested in such systems in and as organisations, and the questions that come up when investigating them as part of complex, cooperative work practices. The full-day workshop, designed for up to 20 participants, advances a CSCW-perspective on algorithmic/AI systems by bringing together researchers within (and where possible beyond) the CSCW community who study algorithmic systems, with the aim of sharing ongoing research and connecting participants with others who share their research interests.
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    Conference Paper
    Deconstructing Gender in Asylum Categories: An Archival Perspective on a Practice with Limited Access
    (Proceedings of 20th European Conference on Computer-Supported Cooperative Work, 2022) Kaltenhäuser, Kristin; Slaats, Tijs; Gammeltoft-Hansen, Thomas; Holten Møller, Naja
    Public authorities make decisions that greatly impact both citizens and non-citizens. Decision-making on asylum, which is regulated by international law but administered by states, in particular is characterised by a higher level of secrecy than other public services. The 1951 Refugee Convention defnes refugeehood as the fear of being persecuted for reasons of race, religion, nationality, social group, or political opinion. Although fear of gender-related persecution was not included as one of the grounds meriting asylum, state practice means that it is today generally recognised as such. The United Nations Refugee Agency (UNHCR) recommends that states "ensure a gender-sensitive interpretation of the 1951 Refugee Convention." Using natural language processing (NLP) to analyse an open dataset of Danish asylum case summaries, we frst identify fve empirical categories connected to gender in the case summaries: 1) gender-related persecution, 2) LGBT 3) sexual conditions, 4) marital conditions and 5) other gender-related forms of persecution. Secondly, we illustrate the relationship between these gender-related categories and other categories/topics in asylum motives. Finally, we discuss how data science techniques can be applied to better understand complex, cooperative work practices in an area where access for researchers is limited, but archival data is available.
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    Conference Paper
    Destabilising Data in Nordic Asylum Decision-making
    (Proceedings of 20th European Conference on Computer-Supported Cooperative Work, 2022) Kaltenhäuser, Kristin
    Asylum decision-making is a complex collaborative work domain. The probability of receiving asylum for individuals from the same country of origin varies signifcantly across states. Research in this area has been conducted in different disciplines, such as legal studies, social sciences and data science. It remains fragmented and applies discrete methodologies that are rarely integrated. I will combine qualitative work/domain expertise and participatory dialogue with computational decision modelling to answer two questions: 1) What factors shape the production of national asylum decisions? and 2) Why do asylum outcomes across similar cases differ so much from one another? I aim to map a set of interdisciplinary methodological and conceptual tools for engaging asylum decision-making data that can lead to the discovery of possible missing data and "counter datasets." In a preliminary study, I investigated gender-related categories of an open dataset of 9.075 Danish asylum case summaries using data science methods and applying an archival perspective. The analytical insights will be used to facilitate the grounded sensemaking of data together with different groups of practitioners.