newtech.law - legal aspects of new technologies

The City of London Corporation announced in October 2017 that it would establish a new court in London for dealing with cybercrimes. This will reaffirm London’s status as a world financial centre and the best place to run businesses and solve disputes. Other states are also considering establishing such courts. In our view, Poland should not fall behind and should set one up too.

Our firm has seen an increase in cybercrime litigation cases, particularly over the last two years. Examples are: manipulating e-mail correspondence in order to change the details of transfers of funds, phishing, security breaches in servers and theft of confidential data, attacks on devices connected to the internet, malware, thefts of cryptocurrencies on deposit with cryptocurrency exchanges and public wallets, and fraudulent operations of algorithms on various websites.

In February 2018 the EU’s Article 29 Data Protection Working Party published its Guidelines on Automated individual decision-making and Profiling for the purposes of Regulation 2016/679. The guidelines explore Art. 21–22 of the General Data Protection Regulation, and although the title may not indicate it, provide another element in the legal framework for development and use of artificial intelligence. They also show that this framework may be truly restrictive.

The General Data Protection Regulation entering into force on 25 May 2018 is not the only privacy revolution in store for the EU. The proposed ePrivacy Regulation is also generating greater and greater controversy and may change the shape of the internet as we know it.

The Financial Stability Board (FSB) was set up in 2009 by members of the G20 to coordinate the work of financial authorities at the international level and promote and develop financial stability policies. It is made up of representatives of state authorities responsible for finances from 24 countries, financial institutions, and supervisory and regulatory authorities.

Several recent meetings and discussions about the blockchain have made me realise the growing antagonism that exists between the law and the blockchain. This conflict is largely the result of misunderstandings, which need prompt clarification.

Many startups offer their clients big data analysis services based on machine-learning algorithms. The results of such analyses can be of interest to any companies profiling their products or marketing campaigns. But for the analysis to be reliable, it takes data—the more the better. Algorithms using machine learning must have something to learn from. The accuracy of the forecasts subsequently developed for business aims will depend on the scope of the training data fed to them. If the algorithm is limited from the start to analysis of an abridged sample of observations, the risk increases that it will incorrectly group data, overlooking important correlations or causal connections—or see them where they don’t exist. Only training the algorithm on large datasets can minimise the risk of shortcomings in diagnosis and prognosis.