accepted abstract Quis14 conference Field findings show that value dimensions in legal services are functional, social and emotional. The last category emerges not only within but also outside the interaction with the lawyer. Recommendation of others or the trackrecord of lawyers for example, which play a role before or after the service, contribute to emotional values like trust and reassurance and help clients to reduce the perceived purchase risk, which is inherent to the nature of credence services. Also due to the credential character of legal services we conclude that not only professional skills but also service aspects as client involvement play an important role in the emergence of value because professional skills are difficult to judge even by routine buyers.
French/English abstract: Les systèmes d’aide à la prise de décision jouent un rôle important dans la pratique juridique aux Pays-Bas. Divers organismes gouvernementaux utilisent de tels systèmes automatisés pour la prise de décisions juridiques (de masse). Les départements juridiques, les cabinets d’avocats, les éditeurs juridiques et d’autres organismes ont de plus en plus recours à ces outils pour appuyer et améliorer les services d’aide juridique aux particuliers et aux entreprises. Ces outils permettent d’améliorer l’efficacité des processus et des services juridiques, mais ils peuvent aussi avoir d’importants effets préjudiciables sur les droits des personnes ou sur la qualité juridique des services produits, en particulier lorsqu’il n’existe pas de processus de conception minutieux et transparent. Cet article donne un aperçu de l’utilisation de ces systèmes dans la pratique juridique néerlandaise, discute de leurs avantages, pièges et défis, puis il identifie certaines questions de recherche pour le futur.---Rule-based systems for decision support and decision-making play an important role in Dutch legal practice. Government agencies use rule-based systems for (mass) legal decision-making. Legal departments, law firms, legal publishers and various other organizations increasingly use rule-basedsystems to support and improve the provision of legal aid to private individuals and corporate clients. Rule-based systems can improve efficiency of legal processes and services, but can also have important detrimental effects on the rights of individuals or legal quality, especially when there is no careful and transparent design process. This article provides an overview of the use of these systems in Dutch legal practice, discusses benefits, pitfallsand challenges and identifies questions for future research.
The HCR-20V3 is a violence risk assessment tool that is widely used in forensic clinical practice for risk management planning. The predictive value of the tool, when used in court for legal decisionmaking, is not yet intensively been studied and questions about legal admissibility may arise. This article aims to provide legal and mental health practitioners with an overview of the strengths and weaknesses of the HCR-20V3 when applied in legal settings. The HCR-20V3 is described and discussed with respect to its psychometric properties for different groups and settings. Issues involving legal admissibility and potential biases when conducting violence risk assessments with the HCR-20V3 are outlined. To explore legal admissibility challenges with respect to the HCR-20V3, we searched case law databases since 2013 from Australia, Canada, Ireland, the Netherlands, New Zealand, the UK, and the USA. In total, we found 546 cases referring to the HCR-20/HCR-20V3. In these cases, the tool was rarely challenged (4.03%), and when challenged, it never resulted in a court decision that the risk assessment was inadmissible. Finally, we provide recommendations for legal practitioners for the cross-examination of risk assessments and recommendations for mental health professionals who conduct risk assessments and report to the court. We conclude with suggestions for future research with the HCR-20V3 to strengthen the evidence base for use of the instrument in legal contexts.
The impacts of tourism on destinations and the perceptions of local communities have been a major concern both for the industry and research in the past decades. However, tourism planning has been mainly focused on traditions that promote the increase of tourism without taking under consideration the wellbeing of both residents and visitors. To develop a more sustainable tourism model, the inclusion of local residents in tourism decision-making is vital. However, this is not always possible due to structural, economic and socio-cultural restrictions that residents face resulting to their disempowerment. This study aims to explore and interpret the formal processes around tourism decision-making and community empowerment in urban settings. The research proposes a comparative study of three urban destinations in Europe (The Hague in the Netherlands, San Sebastian in Spain and, Ioannina in Greece) that experience similar degree of tourism growth. The proposed study will use a design-based approach in order to understand tourism decision-making and what empowers or disempowers community participation within the destinations. Based on the findings of primary and secondary data, a community empowerment model will be applied in one the destinations as a pilot for resident engagement in tourism planning. The evaluation of the pilot will allow for an optimized model to be created with implications for tourism planning at a local level that can contribute to sustainable destinations that safeguard the interests of local residents and tourists.
The ELSA AI lab Northern Netherlands (ELSA-NN) is committed to the promotion of healthy living, working and ageing. By investigating cultural, ethical, legal, socio-political, and psychological aspects of the use of AI in different decision-makingcontexts and integrating this knowledge into an online ELSA tool, ELSA-NN aims to contribute to knowledge about trustworthy human-centric AI and development and implementation of health technology innovations, including AI, in theNorthern region.The research in ELSA-NN will focus on developing and mapping ELSA knowledge around three general concepts of importance for the development, monitoring and implementation of trustworthy and human-centric AI: availability, use,and performance. These concepts will be explored in two lines of research: 1) use case research investigating the use of different AI applications with different types of data in different decision-making contexts at different time periods duringthe life course, and 2) an exploration among stakeholders in the Northern region of needs, knowledge, (digital) health literacy, attitudes and values concerning the use of AI in decision-making for healthy living, working and ageing. Specificfocus will be on investigating low social economic status (SES) perspectives, since health disparities between high and low SES groups are growing world-wide, including in the Northern region and existing health inequalities may increase with theintroduction and use of innovative health technologies such as AI.ELSA-NN will be integrated within the AI hub Northern-Netherlands, the Health Technology Research & Innovation Cluster (HTRIC) and the Data Science Center in Health (DASH). They offer a solid base and infrastructure for the ELSA-NNconsortium, which will be extended with additional partners, especially patient/citizens, private, governmental and researchrepresentatives, to have a quadruple-helix consortium. ELSA-NN will be set-up as a learning health system in which much attention will be paid to dialogue, communication and education.
The value of data in general has become eminent in recent times. Autonomous vehicles and Connected Intelligent Transport Systems (C-ITS), in particular, are rapidly emerging fields that rely a lot on “big data”. Data acquisition has been an important part of automotive research and development for years even before the advent of Internet of Things (IoT). Most datalogging is done using specialized hardware that stores data in proprietary formats on traditional hard drives in PCs or dedicated managed servers. The use of Artificial Intelligence (AI) throughout the world and specifically in the automotive sector is largely reliant on the data for the development of new and reliable technologies. With the advent of IoT technologies, the reliability of data capture could be enhanced and can improve ease of real-time analytics for analysis/development of C-ITS services and Autonomous systems using vehicle data. Data acquisition for C-ITS applications requires putting together several different domains ranging from hardware, software, communication systems, cloud storage/processing, data analytics, legal and privacy aspects. This requires expertise from different domains that small and medium scale businesses usually lack. This project aims at investigating requirements that have to be met in order to collect data from vehicles. Furthermore, this project also aims at laying foundations required for the development of a unified guidelines required to collect data from vehicles. With these guidelines, businesses that intend to use vehicle data for their applications are not only guided on the technical aspects of data collection but also equally understand how data from vehicles could be harvested in a secure, efficient and responsible manner.
