The aim of this research/project is to investigate and analyze the opportunities and challenges of implementing AI technologies in general and in the transport and logistics sectors. Also, the potential impacts of AI at sectoral, regional, and societal scales that can be identified and chan- neled, in the field of transport and logistics sectors, are investigated. Special attention will be given to the importance and significance of AI adoption in the development of sustainable transport and logistics activities using intelligent and autonomous transport and cleaner transport modalities. The emphasis here is therefore on the pursuit of ‘zero emissions’ in transport and logistics at the urban/city and regional levels.Another goal of this study is to examine a new path for follow-up research topics related to the economic and societal impacts of AI technology and the adoption of AI systems at organizational and sectoral levels.This report is based on an exploratory/descriptive analysis and focuses mainly on the examination of existing literature and (empirical) scientific research publica- tions, previous and ongoing AI initiatives and projects (use cases), policy documents, etc., especially in the fields of transport and logistics in the Netherlands. It presents and discusses many aspects of existing challenges and opportunities that face organizations, activities, and individuals when adopting AI technology and systems.
Currently, promising new tools are under development that will enable crime scene investigators to analyze fingerprints or DNA-traces at the crime scene. While these technologies could help to find a perpetrator early in the investigation, they may also strengthen confirmation bias when an incorrect scenario directs the investigation this early. In this study, 40 experienced Crime scene investigators (CSIs) investigated a mock crime scene to study the influence of rapid identification technologies on the investigation. This initial study shows that receiving identification information during the investigation results in more accurate scenarios. CSIs in general are not as much reconstructing the event that took place, but rather have a “who done it routine.” Their focus is on finding perpetrator traces with the risk of missing important information at the start of the investigation. Furthermore, identification information was mostly integrated in their final scenarios when the results of the analysis matched their expectations. CSIs have the tendency to look for confirmation, but the technology has no influence on this tendency. CSIs should be made aware of the risks of this strategy as important offender information could be missed or innocent people could be wrongfully accused.
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.