TATuP – Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis
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Meeting report: “Fair and decent work in the global economy?”. International Labour Process Conference, 2023, Glasgow, UK (hybrid)
Get PDFMeeting report: „Infrastrukturen der Zukunft: Wie kann TA anstehende Transformationen begleiten?“. Conference, 2023, Vienna, AT
Get PDFMeeting report: „Gestreamt, gelikt, flüchtig – schöne neue Kulturwelt? NTA10 ‚Kultur und Digitalisierung‘“. Conference, 2022, Bern, CH
Get PDFAgent-based modeling and simulation in pandemic management
No full textMathematical models and computer simulations play a crucial role in the context of the COVID-19 crisis for knowledge about the possible course of the pandemic and for appropriate policy decisions. The paper presents results from an ethnographic study of a government-funded R & D project dealing with agent-based modeling and simulation (ABMS) in the context of pandemic management. Based on the assumption that the use of computer simulations in pandemic management is not only a means to an end for political or epidemiological goals but also plays a significant role in determining which goals and strategies appear politically legitimate, the paper reconstructs how insights into the pandemic are generated in ABMS and specifically in the researched project and made accessible for decision-making.Mathematical models and computer simulations play a crucial role in the context of the COVID-19 crisis for knowledge about the possible course of the pandemic and for appropriate policy decisions. The paper presents results from an ethnographic study of a government-funded R & D project dealing with agent-based modeling and simulation (ABMS) in the context of pandemic management. Based on the assumption that the use of computer simulations in pandemic management is not only a means to an end for political or epidemiological goals but also plays a significant role in determining which goals and strategies appear politically legitimate, the paper reconstructs how insights into the pandemic are generated in ABMS and specifically in the researched project and made accessible for decision-making
Algorithmic fairness in police investigative work: Ethical analysis of machine learning methods for facial recognition
No full textThis article discusses fairness in artificial intelligence (AI) based policing procedures using facial recognition as an example. Algorithmic decisions based on discriminatory dynamics can (re)produce and automate injustice. AI fairness here concerns not only the creation and sharing of datasets or the training of models but also how systems are deployed in the real world. Quantifying fairness can distract rom how discrimination and oppression translate concretely into social phenomena. Integrative approaches can help actively incorporate ethical, legal, social, and economic factors into technology development to more holistically assess the consequences of deployment through continuous interdisciplinary collaboration.This article discusses fairness in artificial intelligence (AI) based policing procedures using facial recognition as an example. Algorithmic decisions based on discriminatory dynamics can (re)produce and automate injustice. AI fairness here concerns not only the creation and sharing of datasets or the training of models but also how systems are deployed in the real world. Quantifying fairness can distract rom how discrimination and oppression translate concretely into social phenomena. Integrative approaches can help actively incorporate ethical, legal, social, and economic factors into technology development to more holistically assess the consequences of deployment through continuous interdisciplinary collaboration
