interdisciplinary voices in critical computing
our research work and interests
Critical Tools for Machine Learning or CritML is a project that brings together critical intersectional feminist theory and machine learning systems design. The goal of the project is to provide ways to work with critical theoretical concepts that are rooted in intersectional feminist, anti-racist, post/de-colonial […]
In 2022 an increasing number of digital businesses are building their business models on data collection, its usage, and further processing. Data and especially so-called big data holds the potential for a lot of innovation and empowers the advancement of all-day-technologies, knowledge engineering and the […]
Technical research and development are embedded in social contexts, afforded by stakeholders, their goals and interests, values and assumptions, and the social and professional structures they are part of. The choice of research topics and many development decisions start from rather general, standard assumptions about […]
Guest post by Myriam Hernández Domínguez In January 2020 I was a fellow at the fantastic GeDIS group (Gender/Diversity in Informatics Systems). In this post I would like to share a brief summary of my research and how I got to be interested in the […]
In September we – Goda, Loren and Claude – presented a paper at the Society for Social Studies of Science (4S) conference in New Orleans. We were (digitally) part of an amazing panel “Feminist Technoscience by Other Means: Reconfiguring Research Practices for World-Making Beyond the […]
In September this article appeared on GenderIT.org, feminist reflection on Internet policies. Here is a short excerpt from the first part of the article: When I proposed this article, my aim was to share my research and the role of power in participation. I was […]
How to innovate smart homes with design and development stripped of stereotypes? Including gender dimension can help.
How feminist theoretical perspective, particularly situated knowledges and standpoint theory, can help tackle bias in algorithmic systems.