Streszcz. ang. s. 129. Bibliogr. s. 135-136

In a legal context, it is often particularly important to be able to trace the reasons why a decision was made; therefore, there may be an intuition that explainability is extremely important in judicial support systems. However, the standard of explainability (understandability, transparency) that machine learning technologies used to assist judges should meet has not yet been described. Defining this standard is even more complicated because, when considering it, it is necessary to take into account not only the specifics of the legal context in general but also of individual branches of law (for example, criminal or civil law). In this paper, I consider which branches of law, due to their specificity, seem to require the use of the most algorithmically transparent - and thus inherently interpretable - methods. Juxtaposing three general levels of explainability (white boxes, black boxes with post hoc explainers, and full black boxes) with legal values, I consider the paths that the development of machine learning models supporting judicial reasoning should follow in order to be tailored to the specifics of each legal field.