This paper reveals how the automatising of protocols ignited a public conflict between Dutch banks and their Small and Medium-sized Enterprise (SME) clients in the years after the Global Financial Crisis. The bank’s “infirmary departments” for Financial Restructuring and Recovery (FR&R) were accused of (mal)treating SMEs. The conflict resulted in no formal regulatory or legal change despite public support. Instead, the banks created self-regulation to improve communication with SMEs, leading to shifts in governing FR&R for SMEs. This way, the banks mitigated significant negative symptoms of automation and solved the conflict with the SMEs while keeping FR&R and ongoing automation intact. The research uses an interdisciplinary analytical framework to understand national financial conflicts in a digitalised (business) world. It contributes to the theory of institutionalising values in discursive contests between action fields. The paper highlights the material and causes of normative conflicts of interest among critical actors in established public-private networks through discourse analysis and process tracing.
Many attempts have been made to build an artificial brain. This paper aims to contribute to the conceptualization of an artificial learning system that functionally resembles an organic brain in a number of important neuropsychological aspects. Probably the techniques (algorithms) required are already available in various fields of artificial intelligence. However, the question is how to combine those techniques. The combination of truly autonomous learning, in which "accidental" findings (serendipity) can be used without supervision, with supervised learning from both the surrounding and previous knowledge, is still very challenging. In the event of changed circumstances, network models that can not utilize previously acquired knowledge must be completely reset, while in representation-driven networks, new formation will remain outside the scope, as we will argue. In this paper considerations to make artificial learning functionally similar to organic learning, and the type of algorithm that is necessary in the different hierarchical layers of the brain are discussed. To this end, algorithms are divided into two types: conditional algorithms (CA) and completely unsupervised learning. It is argued that in a conceptualisation of an artificial device that is functional similar to an organic learning system, both conditional learning (by applying CA’s), and non-conditional (supervised) learning must be applied.
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