Post #2590342

2026-02-21 23:54 UTC

The use of AI often focuses on enhancing internal efficiency and the application of generative AI. Citizen-oriented applications typically limit themselves to visible chatbots, often based on simple rule-based technology (ADMT), which technically is not AI, or on machine learning (ML), which learns probabilistic patterns from data. Algorithmic systems encompass both approaches and share similar risks and consequences such as bias, discrimination, and a lack of transparency. The most common use cases and motivations for AI in administration are to increase efficiency, automate repetitive tasks, and support staff. There is significant potential for optimizing case processing, improving fraud detection, and risk reduction, as well as a wide range of application possibilities. However, central challenges include data protection, surveillance issues, discrimination, a lack of transparency and explainability, and the potential for flawed decisions. Incorrect AI systems can lead to significant financial and social damage as well as a loss of public trust. Therefore, transparency, legal protection, and human oversight are crucial, especially given the direct impact on citizens' fundamental needs ...

Replies (1)

@loleg@hachyderm.io 2026-02-21 23:54

Human-in-the-loop approaches are not a solution for all problems, as they are limited by time, resources, and expertise. There is a tension between the desire for more efficient, consistent AI decisions and the need for fairness, transparency, and accountability. Incorrect decisions can result in systematic errors and a lack of context consideration. The acceptable risk of errors must be carefully weighed. In Switzerland, there is a lack of comprehensive horizontal AI laws, so the administration relies on existing sectoral regulations (e.g., data protection provisions, specialized laws, internal guidelines). The Council of Europe is planning regulations, but definitions, governance, oversight, and fundamental rights protection remain uncertain. Only a few cantons, such as Aargau and Luzern, have developed comprehensive AI strategies; others deliberately opt out of a comprehensive strategy. The focus often is on operational AI, which directly impacts public value. Not every canton can build its own cloud infrastructure, highlighting the necessity for cooperation and shared solutions. #Winterkongress summary translated with #Apertus

Open ##2590343