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Gehirn-Computer-Schnittstellen beflügeln die Hoffnung auf übermenschliche Kräfte: Sie versetzen Nutzer in die Lage, Prothesen und sonstige Geräte allein mit ihren Gedanken zu steuern. Je weiter die Entwicklung der neuen Technologie voranschreitet und in marktfähige Produkte mündet, desto sichtbarer rücken auch potenzielle Sicherheitsrisiken in den Fokus. Denn Angriffe auf Gehirn-Computer-Schnittstellen können neurologische Daten erspähen oder Gehirnaktivitäten manipulieren und dadurch verheerende Schäden verursachen. Der Beitrag geht der Frage auf den Grund, wie die Rechtsordnung den Risiken eines Angriffs auf Gehirn- Computer-Schnittstellen bislang begegnet – und wie sie ihnen künftig begegnen sollte.
Artificial Intelligence (“AI”) is already being employed to make critical legal decisions in many countries all over the world. The use of AI in decision-making is a widely debated issue due to allegations of bias, opacity, and lack of accountability. For many, algorithmic decision-making seems obscure, inscrutable, or virtually dystopic. Like in Kafka’s The Trial, the decision-makers are anonymous and cannot be challenged in a discursive manner. This article addresses the question of how AI technology can be used for legal decisionmaking and decision-support without appearing Kafkaesque.
First, two types of machine learning algorithms are outlined: both Decision Trees and Artificial Neural Networks are commonly used in decision-making software. The real-world use of those technologies is shown on a few examples. Three types of use-cases are identified, depending on how directly humans are influenced by the decision. To establish criteria for evaluating the use of AI in decision-making, machine ethics, the theory of procedural justice, the rule of law, and the principles of due process are consulted. Subsequently, transparency, fairness, accountability, the right to be heard and the right to notice, as well as dignity and respect are discussed. Furthermore, possible safeguards and potential solutions to tackle existing problems are presented. In conclusion, AI rendering decisions on humans does not have to be Kafkaesque. Many solutions and approaches offer possibilities to not only ameliorate the downsides of current AI technologies, but to enrich and enhance the legal system.