Abstract
Decision-making algorithms (Automated Decision-Making Systems, or ADM) have become an integral, although often unconscious, part of everyday life for each of us. By following suggestions derived from personal profiling facilitated by big data, ADMs make decisions on significant aspects of life, and they are also widely used in family matters and in proceedings involving children, such as decisions regarding child support, custody, and adoption procedures. These practices are widespread in comparative law, both in civil law and common law systems. Their use seems to be justified, on the one hand, by the assumption—even if inaccurate—that an ADM can be fair and independent in its decision-making when, in reality, it absorbs the biases present in the data that fuel its functioning.</p><p>On the other hand, it is argued that their use is economically efficient because they are applied in serial cases, allowing for resource savings. Both assumptions conceal debatable realities that negatively influence algorithmic treatment; generally, data collection concerns not the children but their family or the environment from which they come. As a result, the child is unfairly affected by the negative consequences of their parents' behaviour—those who are actually under scrutiny—but the biases in such data persistently impact the children and their future. The purpose of this abstract is to verify, using a comparative methodology, whether and how legal systems attempt to mitigate the negative impact of family surveillance ADMs on the life and well-being of the minor subject to their scrutiny.