For determining the correctness of an import declaration lodged by a declarant within international shipping, customs authorities rely on manual cross-validation of the declaration with other trade documents. However, the sheer volume of import declarations lodged annually makes it impossible to manually cross-validate each declaration. Smart contracts have the potential to automatically aggregate relevant information from blockchain-based ledgers for lodging and cross-validation. In this paper we explore how such smart contract could be implemented. We demonstrate that a smart import declaration can be used to automatically aggregate information from different sources (e.g., pro forma invoice data elements stored on the blockchain ledger). However, technical and organizational considerations that are inherent to blockchain technology need to be addressed for the smart import declaration to be acceptable for both customs authorities and declarants. It is therefore suggested that future work focusses on data standards required to enable automatic aggregation of relevant trade information and the generation of the import declaration and governance of the smart import declaration by customs authorities to assure correctness.

Nowadays, public debates often take place on social media platforms like Facebook or Twitter and can be characterized as asynchronous, protracted and ill-structured. The Massive Open Online Deliberation (MOOD) platform aims to structure these debates. Essential is that the platform can differentiate between the moral acceptability and the social acceptance of a debate outcome. We briefly describe the e-deliberation process and look at two existing debate platforms, Liquidfeedback and Debatehub. We design and build a prototype that mainly focuses on:(1) a method to differentiate and validate facts and opinions, and (2) a mechanism that maps both the social acceptance and the moral acceptability of debate outcomes. We research these ethical concepts more in depth and implement several techniques, such as a voting mechanism, in a working prototype that supports a four stage deliberation process. In future applications, machine learning techniques can be integrated in the platform to perform sentiment analysis on a debate.