Elena Falletti

Credit scoring software has become firmly established in the banking sector as a means to mitigate defaults and non-performing loans. These software systems pose significant challenges related to their non-transparent nature as well as biases inherent in the data nurturing the machine learning. Despite the Artificial Intelligence Act Proposal not being enacted yet, legal precedents have begun to emerge, starting with the ruling of the Court of Justice of the European Union (Case C-634/21). This ruling acknowledges that individuals seeking bank loans have the right, under Article 22 of the GDPR, to demand an explanation regarding the decision-making process of such programs. This article aims to analyze the evolution of credit scoring software since the SCHUFA ruling and the entering into force of the Artificial Intelligence Act.

A critical aspect of modern healthcare involves recognizing and addressing pharmaceutical needs. Predictive models serve as valuable decision-making tools in the healthcare sector to proactively prevent supply chain failures. However, training these models on real historical data to reliably reflect actual demand is a delicate process. An effective model, capable of estimating the amount of drugs to be distributed in relation to the patient’s needs, must be accurate and inherently fair. Our study endeavors to bridge legal perspectives on fairness with practical assessments of algorithmic fairness, specifically in the context of predicting drugs to be distributed in a specific studied area of reference. An in-depth overview of the Italian National Healthcare Service is provided, emphasizing its regulatory role in drug dispensation and its inherent challenges. Furthermore, a review of fundamental bias research principles is provided, encompassing legal and statistical viewpoints. In addition, a comprehensive Exploratory Data Analysis is conducted using real world data, to highlight challenges that can be encountered in the initial modeling phase. The results of the analysis reveal the presence of missing values in some of the most relevant fields, and signal differences in the drugs distribution patterns between the two genders for specific therapeutic groups. Such disparities would require further investigations to verify the presence of social bias. These findings contribute to an in-depth understanding of patient populations concerning drug collection. Importantly, our study promotes a comprehensive approach that incorporates legal considerations and technical elements to improve the fairness and efficacy of predictive models in healthcare.

The theme of self-produced weapons intertwines diversified ideas of an ethical, legal, engineering and data science nature. The critical starting point concerns the use of 3D printing for the self-production of weapons: the doctrinal and ethical discussion is open, while from a case-law point of view no published decisions have been found. From a technical point of view it should be noted that, being produced with materials other than metal, the weapons in question would increase their danger, since it would not be possible to ascertain their possession through metal detectors. This possibility demonstrates how the combination of the application of 3D printing and AI can lead to further development of Autonomous Weapon Systems, especially drones, which are no longer confined to science fiction novels, but may appear on the market for goods and even become available for mass consumption, and it stresses the need for the promotion of negotiations for the drafting of an international treaty banning the production and use of lethal autonomous weapons. The combination of such printers with biometric facial recognition algorithms raises concerns for the increasing issues of physical , individual and collective safety that may arise. In fact, the biometric recognition technology allows the identification of individuals through the measurement and analysis of the somatic or behavioural traits; it is based on intelligent software, modelled on the human ability to recognize and identify faces by collecting and analysing huge amounts of data, and it is able to evolve its skills beyond its programmer's initial intention. It is clear that allowing self-production of such devices by non-expert users could produce more damages than benefits. The purpose of this contribution is to study how to regulate the effects of such self-made autonomous robots, since their use may have a devastating and disruptive effect on public integrity and social peace, especially in case of violent riots.

In this paper we propose a fuzzy logic-based approach to analyze UK National Health Service (NHS) public administrative data related to pre-and post-pandemic claims filed by patients, analyzing the legal and ethical issues connected to the use of Artificial Intelligence systems, including our own, to take critical decisions having a significant impact on patients, such as employing computational intelligence to justify the management choices related to Intensive Care Unit (ICU) bed allocation. Differently from previous papers, in this work we follow an unsupervised approach and, specifically, we perform an analysis of UK hospitals by means of a computational intelligence algorithm integrating Fuzzy C-Means and swarm intelligence. The dataset that we analyse allows us to compare pre-and post-pandemic data, to analyze the ethical and legal challenges of the use of computational intelligence for critical decision-making in the health care field.

The COVID-19 pandemic has generated an overall slowdown in hospital activities that might lead to delays in healthcare interventions, and the scarcity of resources can raise concerns about ventilators allocation criteria. These circumstances could lead to lawsuits against hospitals and healthcare professionals: together with Regions and States, they may be vulnerable to legal actions, due to the breach of right to health, to physical integrity and right to life, to the manifestation of the informed consent in the medical field or on the basis of contractual or Aquilian obligations. In this context, predicting the litigation rate could be useful to assess the economic impact of a dispute at a local and national level, so that hospital managers and public institutions can perform multi-dimensional and cost/benefit evaluations to decide whether to invest resources to increase critical care surge capacity. In this work we present CLIP (COVID-19 LItigation Prediction), a modeling approach supported by swarm intelligence designed to forecast the occupancy of intensive care units using COVID-19 time-series. CLIP fits a logistic model of COVID-19 patients admission in order to estimate the future number of patients, and then exploits a probabilistic model to predict the number of occupied intensive care beds, whose parameters are calibrated by means of Fuzzy Self-Tuning Particle Swarm Optimization. We assume that each individual rejected from an intensive care unit due to the lack of resources should be considered a potential plaintiff. The development and the availability of such a predictive model, that could further be used within other clinical conditions and important diseases, could help policy-makers in taking decisions under conditions of uncertainty.

The aim of this paper regards the analysis of privacy management of wearable devices (hereinafter "WD") absorbing personal data from a user's body and from his or her behavior. There are many discussions on WD privacy management. On the one hand, some people affirm that privacy, in an age of invasive electronic communication, should be a fundamental right. On the other hand, other views support that privacy has to be treated as an additional service that the user can buy if markets are interested in it. Indeed, the strict contact between the user's body and the wearable device may give the impression that privacy is a "plus" service included with the WD product. However, privacy regards an individual fundamental right, and personal data collected through WD (or through other devices that are in close contact with user's body and life) have to be strongly protected.

Some children at a very young age could feel, in the deepest realm of self-perception, to belong to the opposite sex, in spite of their absolute biological normality. According to medical literature this phenomenon is defined "Atypical Gender Identity Organization" (A.G.I.O). The DSM-5 provides different criteria for the diagnosis of gender dysphoria in children than in adults and adolescents. It was highlighted that the variance of gender identity can be defined as the state in which gender identity is organized atypically during the psychosexual development of a child. These are cases in which children and adolescents perceive their sexual identity as inappropriate to their body and then to their biological sex. They may feel unhappy regarding their physical and sexual features and express a desire to be recognized as belonging to the opposite sex. Such situations are accompanied by difficulties from an emotional perspective, with significant behavioral suffering associated with their conditions. Medical experience shows that about 1/3 of those children affected by A.G.I.O. will be oriented to surgical intervention to change their sex, while the other 2/3 will overcome their perceived identity conforming to the biological sex during their pubertal development. After becoming adults half of the latter will have a heterosexual orientation and the other half will evolve towards a homosexual orientation. In rare cases, only after thorough psychological assessment, the international literature admits the possibility to carry out puberty blocking therapy and, subsequently, to a cross sex state. These are the cases considered appropriate to make the choice about possible drug treatment according the guidelines of the Endocrine Society issued in 2009 and ratified by WPATH (World Professional Association for Transgender Health) in 2011. These therapies help to provide more time for both professionals and young people to better understand what to do without worries about the development of sexual characteristics of the biological sex. In this sense, the desired role allowing a smoother inclusion of affected teenager in his or her social environment.The condition of children suffering from atypical organization of gender identity directly involves the relationship between parenting choices and protection of the preeminent best interest of the child, first of all his or her mental well-being, as well as to the manifestation of consent to administration of drug therapies It must be considered that in psychiatry A.G.I.O. is still considered a mental disorder, while in theory there are those who states that it is a medical convention detrimental to the personalistic principle of self-determination. The purpose of this paper is to analyze the comparative legal sources and case law concerning the manifestation of informed consent to medical treatment and the legal consequences related to gender change.