Unitarism vs. Individuality and a New Digital Agenda: The Power of Decentralized Web

Abstract

Discussions around Covid-19 apps and models demonstrated that primary challenges for AI and data science focused on governance and ethics. Personal information was involved in building data sets. It was unclear how this information could be utilized in large scale models to provide predictions and insights while observing privacy requirements. Most people expected a lot from technology but were unwilling to sacrifice part of their privacy for building it. Conversely, regulators and policy makers require AI and data science practitioners to ensure optimal public health, national security while avoiding these privacy-related struggles. Their choices vary largely from country to country and are driven more by cultural aspects, and less by machine learning capabilities. The question is whether current ways to design technology and work with data sets are sustainable and lead to a good outcome for individuals and their communities. At the same time Covid-19 made it obvious that economies and societies cannot succeed without far-reaching digital policies, touching every aspect of how we provide and receive education, live, and work. Most regions, businesses and individuals struggled to benefit from competitive capabilities modern data technologies could bring. This opinion paper suggests how Germany and Europe can rethink their digital policy while recognizing the value of data, introducing Data IDs for consumers and businesses, committing to support innovation in decentralized data technologies, introducing concepts of Data Trusts and compulsory education around data starting from the early school age. Besides, it discusses advantages of data-tokens to shape a new ecosystem for decentralized data exchange. Furthermore, it emphasizes the necessity to develop and promote technologies to work with small data sets and handle data in compliance with privacy regulations, keeping in mind costs for the environment while bidding on big data and large-scale machine learning models. Finally, innovation as an integral part of any data scientist's job will be called for.