With the emergence of remote education and work in universi- ties due to COVID-19, the ‘zoomification’ of higher education, i.e., the migration of universities to the clouds, reached the public dis- course. Ongoing discussions reason about how this shift will take control over students’ data away from universities, and may ulti- mately harm the privacy of researchers and students alike. How- ever, there has been no comprehensive measurement of universi- ties’ use of public clouds and reliance on Software-as-a-Service of- ferings to assess how far this migration has already progressed. We perform a longitudinal study of the migration to public clouds among universities in the U.S. and Europe, as well as institutions listed in the Times Higher Education (THE) Top100 between Jan- uary 2015 and October 2022. We find that cloud adoption differs between countries, with one cluster (Germany, France, Austria, Switzerland) showing a limited move to clouds, while the other (U.S., U.K., the Netherlands, THE Top100) frequently outsources universities’ core functions and services—starting long before the COVID-19 pandemic. We attribute this clustering to several socio- economic factors in the respective countries, including the general culture of higher education and the administrative paradigm taken towards running universities. We then analyze and interpret our results, finding that the implications reach beyond individuals’ pri- vacy towards questions of academic independence and integrity.@en

Researchers and practitioners often face the issue of having to attribute an IP address to an organization. For current data this is comparably easy, using services like whois or other databases. Similarly, for historic data, several entities like the RIPE NCC provide websites that provide access to historic records. For large-scale network measurement work, though, researchers often have to attribute millions of addresses. For current data, Team Cymru provides a bulk whois service which allows bulk address attribution. However, at the time of writing, there is no service available that allows historic bulk attribution of IP addresses. Hence, in this paper, we introduce and evaluate our ‘Back-to-the-Future whois’ service, allowing historic bulk attribution of IP addresses on a daily granularity based on CAIDA Routeviews aggregates. We provide this service to the community for free, and also share our implementation so researchers can run instances themselves.@en

This roundtable discussion, sponsored by a SSHRC Connection Grant, brings together four international faculty members from a range of academic and industry backgrounds in engineering and social sciences to discuss how they engage with equity and social justice issues in their work, focusing specifically on methodology and how students and young professionals can approach these issues. Ansari will describe his current efforts to decolonize design research in the university community, in particular through the_Decolonising Design_platform. Gürses will discuss her ongoing work in the field of Privacy Engineering, which focuses on designing, implementing, adapting, and evaluating theories, methods, techniques, and tools to systematically capture and address privacy issues in the development of sociotechnical systems. Hoffman will focus on a novel and timely intervention into Data Ethics: Feminist Data Ethics, which engages with the ethical implications of data's production, circulation, application, and storage. Sloane will highlight the critical importance of responsible AI design and governance, interdisciplinary opportunities for researchers to develop and implement tools to engage with responsible innovation, innovation in AI procurement, and AI auditing. @en

Algorithmic fairness aims to address the economic, moral, social, and political impact that digital systems have on populations through solutions that can be applied by service providers. Fairness frameworks do so, in part, by mapping these problems to a narrow definition and assuming the service providers can be trusted to deploy countermeasures. Not surprisingly, these decisions limit fairness frameworks' ability to capture a variety of harms caused by systems.
We characterize fairness limitations using concepts from requirements engineering and from social sciences. We show that the focus on algorithms' inputs and outputs misses harms that arise from systems interacting with the world; that the focus on bias and discrimination omits broader harms on populations and their environments; and that relying on service providers excludes scenarios where they are not cooperative or intentionally adversarial.
We propose Protective Optimization Technologies (POTs). POTs, provide means for affected parties to address the negative impacts of systems in the environment, expanding avenues for political contestation. POTs intervene from outside the system, do not require service providers to cooperate, and can serve to correct, shift, or expose harms that systems impose on populations and their environments. We illustrate the potential and limitations of POTs in two case studies: countering road congestion caused by traffic beating applications, and recalibrating credit scoring for loan applicants.@en