## 4 Validation and Consequences
### CI and Data  
AI/CI concepts will rely on the availability of data to learn from and use in their applications, usually vast amounts of data. This requires the preparation and assessment of available or emerging databases and the creation of the necessary:  
* Organizational and technical structures to maintain and communicate the relevant data;
* Political/legal prerequisites, including those on data propriety and privacy regulations, access rights and responsibilities;
* Technical solutions to harmonize data from various sources;
* Verification and validation of data;
* Intelligent compression of large amounts of data.  
Data compression and information theory are not only important for saving storage space and channel capacity for transmissions, they also play a key role in modern approaches to artificial intelligence [16].  
We will not be able to discuss AI using complex data sources within this article. Nevertheless, it is common understanding that all practical and useful AI applications begin with data. As an example of AI in manufacturing, see, e.g., [[https://www.forbes.com/sites/willemsundbladeurope/2018/10/18/data-is-the-foundation-for-artificial-intelligence-and-machine-learning](https://www.forbes.com/sites/willemsundbladeurope/2018/10/18/data-is-the-foundation-for-artificial-intelligence-and-machine-learning)]([https://www.forbes.com/sites/willemsundbladeurope/2018/10/18/data-is-the-foundation-for-artificial-intelligence-and-machine-learning](https://www.forbes.com/sites/willemsundbladeurope/2018/10/18/data-is-the-foundation-for-artificial-intelligence-and-machine-learning)) (accessed on 20 May 2021).  
A typical classification scheme of \"big data\", as seen today in the era of internet with social media, with cloud services and many others, is depicted in Figure 2.  
The top categories in this picture, Velocity, Volume and Variety of data, are only three of the most important technical classification criteria for mass data characterization.  
Further data characteristics need to include:  
* Data sources and capturing, for instance, from sensors (cameras, RFID, etc.), organizations, individuals, software systems;
* Processes of capturing, transferring, structuring and storing data;
* Processing, analyzing and validating contents;
* Maintenance and quality management, including authenticity, correctness, reliability and scalability;
* Security and data protection, which are one of the most sensitive precautions needed (see also Section 6.2 on security);
* Socio-political aspects of data use; see, e.g., [PERSON] (2017), big data, artificial intelligence, machine learning and data protection, Version: 2.2 [18];
* Scalability and durability of databases.  
Among this rich spectrum are the key requirements concerning data that will be used for AI. When we discuss the applications of AI/CI in the social context, as we do in this article, we believe that, for preparation at the socio-political level, certain elements are indispensable:  
1. Public authorities: Public institutions and actors such as healthcare, security and transportation must agree and prepare to apply their data to novel solutions in AI;
2. Public-private cooperation: A consensus should be reached among public authorities with private big data resources. This, hopefully, will be reached in a consensus-creating environment. In the best case scenario, we hope for public-private win-win strategies, but reinforcement by regulations may become necessary in order to create and maintain a balance of power between governments and private industries;
3. International agreements, in particular when preparing for global AI applications, e.g., for security, climate control, population growth control, and the distribution of global resources or global rules for market and finance. These efforts require political initiatives and need to be started now and sustained and supported by impartial research capacities.