UNBLOCK: The Potential of Blockchain for Social Innovation

United Kingdom, June 29, 2022

London UK

UNBLOCK: The Potential of Distributed Ledger (Blockchain) Technologies for Socio-Economic Innovation.

 

About this event

Call for Papers

The purpose of the UNBLOCK conference is to bring together academics, practitioners and their international networks to investigate the implications of Distributed Ledger (Blockchain) Technology for socio-economic innovation through a three-day international symposium held at the University of Leicester.

We encourage submissions by computer scientists, economists, management scholars, sociologists, legal and communication experts, but we also wish to synergise other interconnected areas of study, in order to produce significant knowledge in relation to:

  1. Enhancing technical aspects of block-chained enabled data integration to ensure reproducibility, traceability, and compliance, including scalable TREs for accessing and hosting this type of data; blockchain-enabled trust and privacy-aware data science.
  2. Examining the Corporate Social Responsibility (CSR) agenda in relation to Distributed Ledger Technologies (DLT), such as development methods for DLT and smart contract applications, including their institutional and technical design parameters, quality assurance and modes of governance, identifying barriers and facilitators to the adoption of blockchain solutions to CSR challenges, interrogating marketing and branding aspects of DLT promotion.
  3. Understanding valorization on digital intermediation platforms by analyzing the production process, services and contents provided, innovations in terms of partnerships, business models, ownership, and labour organization in blockchain-enabled projects.
  4. Elaborating on branding and promotional mechanisms and distribution channels of blockchain technologies amongst several institutional actors and within governmental, private and commercial settings.
  5. Developing DLT to mitigate the impact of algorithmic bias due to socio-economic background, race, gender, ethnicity; contributing to people centred data science where people engage in the democratic process and have control over their own data.