GenTec 2025
GenTec 2025 is an interdisciplinary workshop series on Gender and Technology, organized by the Society of Women in Social Sciences and Humanities (SWiSH) in collaboration with the Computer Science Network of Women (CSNOW), the Digital Society Initiative (DSI) and ETH Diversity.
About
This interdisciplinary series delves into the ways gender and technology interact within contemporary society. With a focus on feminist perspectives and empirical research, we will examine different forms of technologies and their interaction or impact on gender, race, class, among others. The series aims to promote critical dialogue and offer empirical and practical insights that are relevant to fostering inclusive design strategies. Key themes include intersectionality, digital equity, and the impact of technologies on marginalized communities.
Event Details
Date: Every Tuesday March-April 2025
Time: 12:00-13:15
Location: Digital Society Initiative, SOC building, R?mistrasse 69, Zürich
Room: Schr?dinger (1st floor). Online participation is possible, link will be provided upon registration
Registration
external page Please register here
Lunch will be provided to participants who register. It is possible to participate from remote, links will be sent to registered participants.
Programme
Technologies, Gender and Biases: An Introduction
Workshop with Dr. Michele Loi (he/him), Senior Scientific Manager at AlgorithmWatch.
This introductory workshop will explore the intersection of gender and technology, focusing on the potential biases that arise within different technological tools. We will examine key topics such as diagnostic statistics, fairness metrics, and the ethics of decision-making in contexts like fraud detection, where algorithmic outcomes impact legal judgments. Participants will engage with foundational principles of fairness in prediction-based decisions, including counterfactual conditions for identifying discrimination and assessing the alignment of outcomes with justice.
Case studies, such as the fairness of fraud detection algorithms, will highlight challenges in ensuring equitable treatment, especially in light of social and moral arbitrariness. Through discussions of causal discovery and representation in algorithms, the workshop will provide a deeper understanding of how technologies can perpetuate or mitigate bias in decision-making processes.
Matching algorithms: A comparative study of algorithmic–user classification practices in online dating: a human-machine learning process
Workshop with Dr. Jessica Pidoux (she/her), Digital Sociologist at the Institute of Sociology, University of Neuch?tel and Director of external page PersonalData.IO
Abstract tba
The Platformed Migrant: Belonging, agency, and voice in algorithmic spaces of oppression
Workshop with Dr. Daniela Jaramillo-Dent (she/her),?Department of Communication and Media Research (IKMZ), University of Zurich
Social media has enabled unprecedented visibility to communities traditionally marginalized by legacy media industries. Far from a democratized space for self-representation, social media platforms configure new structural inequalities deriving from the neoliberal logics of platformization and dominant actors defining what is acceptable. Based on a digital ethnography with communities of Latin American TikTokers in United States and Europe including interviews and non-participant observations, this presentation outlines the ways in which migrant creators adapt their content to fit the established genres within the platform considering the grammars, logics, policies and politics that characterize it. This presentation will examine the challenges and opportunities of platformed storytelling for underrepresented groups such as migrants as well as the different layers of discrimination that these communities face.
Contamination as Queerness: Challenging Normative Violence in AI—From Facial Recognition to Machine Translation
Workshop with Qingyi?Ren (they/them), Researcher at Critical Media Lab Basel, Doctoral student at the MAKE/SENSE Program
Data contamination is often seen as a flaw, but this talk reimagines it as a form of queerness—disrupting the ideal of "clean" data and rigid knowledge structures. Drawing on queer theory and Anna Tsing’s concept of "generative contamination," I explore how data pollution can act as a subversive force within computational systems. Through case studies in facial recognition and machine translation, I demonstrate how contamination challenges binary gender norms and fosters inclusivity. By embracing contamination as creativity, we can build more adaptive, diverse, and transformative AI.