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Fair, Transparent and Explainable Decision Making in a Juridical Case
Journal article(2022)
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Maaike H.T. de Boer, Steven Vethman, Roos M. Bakker, Ajaya Adhikari, Michiel Marcus, Joachim de Greeff, Jasper van der Waa, Emma M. van Zoelen, Bart Kamphorst, More Authors...
The goal of the FATE system is decision support with use of state-of-the-art human-AI co-learning, explainable AI and fair, secure and privacy-preserving usage of data. This AI-based support system is a general system, in which the modules can be tuned to specific use cases. The FATE system is designed to address different user roles, such as a researcher, domain expert/consultant and subject/patient, each with their own requirements. Having examined a Diabetes Type 2 use case before, in this paper we slightly iterate the FATE system and focus on a juridical use case. For a given new juridical case the relevant older court cases are suggested by the system. The relevant older cases can be explained using the eXplainable AI (XAI) module, and the system can be improved based on feedback about the relevant cases using the Co-learning module through interaction with a user. In the Bias module, the use of the system is investigated for potential bias by inspecting the properties of suggested cases. Secure Learning offers privacy-by-design alternatives for functionality found in the aforementioned modules. These results show how the generic FATE system can be implemented in a number of real-world use cases. In future work we plan to explore more use cases within this system.
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The goal of the FATE system is decision support with use of state-of-the-art human-AI co-learning, explainable AI and fair, secure and privacy-preserving usage of data. This AI-based support system is a general system, in which the modules can be tuned to specific use cases. The FATE system is designed to address different user roles, such as a researcher, domain expert/consultant and subject/patient, each with their own requirements. Having examined a Diabetes Type 2 use case before, in this paper we slightly iterate the FATE system and focus on a juridical use case. For a given new juridical case the relevant older court cases are suggested by the system. The relevant older cases can be explained using the eXplainable AI (XAI) module, and the system can be improved based on feedback about the relevant cases using the Co-learning module through interaction with a user. In the Bias module, the use of the system is investigated for potential bias by inspecting the properties of suggested cases. Secure Learning offers privacy-by-design alternatives for functionality found in the aforementioned modules. These results show how the generic FATE system can be implemented in a number of real-world use cases. In future work we plan to explore more use cases within this system.
Blockchain-based applications can enhance the sharing of information in processes involving multiple types of stakeholders, as in Supply Chain Management (SCM). A supply chain network can benefit from the visibility of the flow of goods, money, and information enabled by blockchain technology. So far, only limited evidence is available on the perspective of Small and Medium-sized Enterprises (SMEs) on the adoption of blockchain-based applications in their business processes. This may lead to a heavily diminished role for SMEs in SCM if large consortia unilaterally decide to adopt a blockchain-based architecture to improve the performance of the entire supply chain. Therefore, we conducted a study to explore which factors influence SMEs’ intention to adopt blockchain technology. Based on a literature review into technology adoption frameworks, we derived the technological, organizational, and environmental (TOE) factors that can play a role in their decision-making process. We distributed a survey amongst European SMEs using the multi-criteria decision-making method of the Best-Worst Method (BWM) to elicit the relative weights of these factors. The data analyses show that in contrast to other studies into technology adoption, the SMEs’ intention to adopt blockchain-based applications is primarily influenced by organizational rather than by technological and environmental factors. This implies that SMEs are best supported by showing blockchain’s organizational benefits and by training senior executives at their company.
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Blockchain-based applications can enhance the sharing of information in processes involving multiple types of stakeholders, as in Supply Chain Management (SCM). A supply chain network can benefit from the visibility of the flow of goods, money, and information enabled by blockchain technology. So far, only limited evidence is available on the perspective of Small and Medium-sized Enterprises (SMEs) on the adoption of blockchain-based applications in their business processes. This may lead to a heavily diminished role for SMEs in SCM if large consortia unilaterally decide to adopt a blockchain-based architecture to improve the performance of the entire supply chain. Therefore, we conducted a study to explore which factors influence SMEs’ intention to adopt blockchain technology. Based on a literature review into technology adoption frameworks, we derived the technological, organizational, and environmental (TOE) factors that can play a role in their decision-making process. We distributed a survey amongst European SMEs using the multi-criteria decision-making method of the Best-Worst Method (BWM) to elicit the relative weights of these factors. The data analyses show that in contrast to other studies into technology adoption, the SMEs’ intention to adopt blockchain-based applications is primarily influenced by organizational rather than by technological and environmental factors. This implies that SMEs are best supported by showing blockchain’s organizational benefits and by training senior executives at their company.
