Bias does not equal bias

Abstract

schweig & Hochschule Emden Leer) spoke about a framework for examining AI-based health apps for diagnosis. They will use feminist concepts of agency from Science and Technology Studies (STS) as framework in their upcoming ethnographic study on classifications and biases in data. K. Napiwodzka and K. Ci-erszko (Adam Mickiewicz University, Poznań) asked whether AI could provide a safe space for female body politics in the highly contested political environment in Poland. P. Martin and J. Ding (University of Sheffield) presented the repurposing of common drugs for the treatment of rare diseases as a possible use of AI in medical research. The assistance of AI could accelerate and economize the approval process and improve access to therapy. W. Ernst (Johannes Kepler University, Linz) asked if standards of medical research can be questioned with AI and pointed out issues such as: Who can be the representative of who's body? How are categories envisioned? And to whose benefit? Fairness and diversity in medical AI. C. Kropp and K. Tampe-Mai (University of Stuttgart) considered 'accessibility' as a crucial point for social in/justice in AI based smart healthcare systems. The constraints to be considered are financial access, usability and digital health literacy. S. Morais dos Santos Bruss (TU Dresden) used a feminist-decolonial perspective to explore "surrogate"-robotics and the care-revolution. H. Drukarch (Lei-den University) showed how a lack of diversity for AI in medicine means erasure, exclusion and silencing of minorities. However , data can also construct (new) normalities when presented as facts and reproduce and naturalize social categories. Postcolonial perspectives. K. Vlantoni and K. Papanastasiou (National and Kapodistrian University of Athens) analyzed expectations concerning the integration of medical AI in Greece against the background of technological enthusiasm and nationalism. S. Mbelu (Erasmus University Rotterdam) reflected on how to design and provide AI enabled health insurance platforms in Nigeria without the pitfalls the Global North has experienced. He concluded that new technologies may have the potential to add important value, e. g. for healthcare universalism, but also exacerbate health disparities to the detriment of the most vulnerable. With the example of Native American Tribes, T. Hendl and T. Roxanne (LMU Munich) pointed out risks of using digital surveillance for racialized minorities during the COVID-19 pandemic. They argued for the inclusion of and respect for indigenous perspectives and indigenous data sovereignty. Discourses and knowledge production. The third day of the conference started with K. Wiggert's (TU Berlin) study on data driven clinical decision support systems for cardiology-related diseases that allow physicians to simulate effects of different treatment strategies. The tools reshape medical reasoning and decision-making. However, physicians who were involved in the process ultimately did not feel represented by the tool. Collaborations between engineers and physicians throughout the development process thus should be based on the needs of physi-OPEN ACCESS The interdisciplinary conference Fair medicine and artificial intelligence was held at the Center for Gender and Diversity Research, University of Tübingen, from 3-5 March. About 70 participants from the social sciences, philosophy, and medical ethics developed socio-technical perspectives on artificial intelligence (AI) and machine based applications as well as deep learning technologies in the medical and healthcare sectors. The big promises for possible future applications of AI in the professional medical field, e. g. diagnosis, prognosis, and therapy recommendations, lead to assumptions that AI will soon play an important role in the health sector and will help to address healthcare disparities, which are currently posing an individual threat, a threat to social justice and a major challenge to the healthcare system. AI could, for instance, reveal human bias, provide more equal treatment to all patients, make health care more accessible and identify possibilities of improvement to develop a more just healthcare system. However, critical voices warn that AI might heighten existing inequalities as technical complexities make them harder to detect. Socio-technical perspectives Future applications of AI. E. Detfurth (York University) talked about AI data-driven applications for dementia care. She showed how classification systems of data repositories and brain atlases find their way into AI tools and described chances and limitations of AI-assisted dementia diagnosis. A. K. Kühnen (TU Dresden) focused on the question of representation of BIPOC, concluding that AI might reproduce and exacerbate inequalities between "white" and non-white racial groups. When analyzing racial bias, technological as well as economic, historical and biopolitical aspects need to be considered. C. Bath and S. Samerski (TU Braun-69 REFLECTIONS This is an article distributed under the terms of the Creative Commons Attribution License CCBY 4.0 (https://creativecommons.org/licenses/by/4.0/) https://doi.