Germany Update: Concepts of digitalization with relevance for the company law of the future

Increasing digitalization is changing the economy, working life, and all entrepreneurial activity; it ultimately covers the whole of society. With the digital world came numerous new terms and word creations, which have already changed the corporate world in a lasting way. This article explains individual terms and shows their relevance for the company law of the future.

The collective term “artificial intelligence” (AI)

The term “artificial intelligence” or “AI” is already so common that one might think that there is a universal definition. However, the term is at best a collective term: artificial intelligence is mentioned, for example, when algorithms are used, the data is analyzed and evaluated according to certain specifications. Also so-called “machine learning” is discussed under the umbrella term of AI. The further development of robotics, for example, in connection with autonomous driving, will also be discussed under AI. The above list is not exhaustive.

From a corporate law perspective, AI is of interest from different angles: On the one hand, the use of AI opens up possibilities for optimizing legally determined processes in the company, for example in the area of risk management for the detection of patterns of probable rule violations, as part of due diligence, for analyzing the company’s financial data, etc. At management level, the use of intelligent information-gathering systems as a basis for making business decisions is also likely to become increasingly relevant (keyword “big data”, in this regard immediately).

Apart from this, however, there are also new liability issues in the context of AI, both at the level of the company and with regard to the liability of the management, if the business decision made on the basis of an AI-based data analysis proves problematic. It is therefore high time to look more closely at the terms and their legal relevance.

Algorithms

The term algorithm is known from mathematics and initially only describes an (IT-supported) calculation process according to a specific, repetitive scheme. Algorithms are able to independently recognize patterns, laws, and correlations in large amounts of data and to “act” accordingly. However, they themselves only carry out predetermined processes; the result is a certain degree of automation but within the limits of what is specified by the programmer.

Algorithms have been used in the stock exchange trading for some time and have also found their way into the legal regulations there (e.g. in Section 80 (2) of the WpHG). In addition, they are likely to have gained their greatest notoriety in connection with Internet search engines and social media: algorithms suggest new purchase objects, messages, messages or videos that correspond to previous searches and where the algorithm assumes that they arouse our interest. Algorithms are therefore always used where large amounts of data are to be analyzed.

It is discussed that decisions within companies, in particular the decisions of the management, could also be supported by algorithms in the future or, if necessary, even made. In this respect, however, numerous follow-up questions arise: Who is liable for the erroneous decision of the algorithm or for the erroneous algorithm? What are the consequences if a manager does not understand the algorithm’s own logic or if the database of the algorithm was insufficient?

The use of algorithms could also be important in the area of disempowerment for erroneous business decisions: Especially when strategic decisions have to be made on a complex or extensive data basis, algorithms could be superior to human decision-makers or at least secure their decision.

Machine learning or machine learning (also: Deep Learning)

“Machine learning” refers to a process in which an artificial system (a “machine”) derives patterns, correlations or laws from training data, thereby enabling it to generalize them. Two approaches are distinguished: one is based on algorithms, the other on so-called neural networks.

In particular, systems based on neural networks should also be able to assess unknown data according to what they have learned. In contrast to the pure algorithm, the suitably trained machine is thus able to make an autonomous decision based on the learned models, which is not necessarily predictable from a human point of view and is also not easily comprehensible (problem of the so-called “black box”). The fields of application seem endless, both in terms of operational activity and in terms of company organization. In addition to more specific areas such as financial market analysis, product development, automated diagnostic procedures, speech recognition systems, translation programs, etc., almost all areas of the company organization are mentioned as fields of application: risk management, compliance, personnel, and applicant selection, purchasing, production planning, marketing, etc. Machine learning, therefore, has considerable economic potential for smarter decisions. The downside is (liability) risks and problems of provability when it is unclear how the machine came to a certain conclusion.

Big data

At first, big data simply refers to the ever-faster growing amount of data worldwide. In addition, big data or big data analytics are especially known as technologies for the rapid processing and evaluation of huge and complex amounts of data. It is a question of particularly high-performance IT systems that enable companies (or governments and authorities) to use and process information and data that is available internally or externally for their purposes.

In close connection with the big data issue, there is the need to rethink the topics of data availability, data collection, data storage, and data security. Global competition for available data could be difficult to attract, especially for small and medium-sized enterprises. Then there is the question of data quality and transparency. The results of data analysis by artificial systems are likely to depend to a large extent on the quality of the data evaluated, on the one hand in terms of their accuracy, truth, and accuracy: do the data to be evaluated actually provide an answer to the question that arises? What is the specific objective of data analysis? On the other hand, however, there is also regard to the legal usability of the data. The data must not be obtained through violations of data protection regulations, competition or copyright law, and possibly also against labor law. In this respect, key questions arise of the liability of the management with regard to sufficient information and decision preparation as well as compliance management. In the future, the Supervisory Board’s supervisory activities will also cover the company’s use of AI.

Result

The digitalization of the economy, society, and working life are already well advanced. However, the legal classification of these new technologies are still in their infancy. Legal questions also arise for company law and, in particular, for management, in connection with the use of artificial intelligence, which can hardly yet be regarded as settled. From the point of view of the management, in particular, it, therefore, makes sense to follow developments closely from the outset, so that the company does not miss the connection to current technical developments and can implement them in accordance with the law.

By Dr. Victoria Berger, MELCHERS, Germany, a Transatlantic Law International Affiliated Firm. 

For further information or for any assistance please contact germany@transatlanticlaw.com

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