Projects

Unibo involved Department/s:
Department of Legal Studies


Overseen by Professor Luciano Floridi, this project is designed to critically evaluate current approaches by the member states of the Global Digital Health Partnership to investing in both the development of the ‘AI technologies’ themselves and the development of the ‘Governing’ ecosystem. We will evaluate the different approaches against a set of ‘success criteria’ and also capture the ‘ethical, legal and social’ impacts of the proposed investments and policy developments.

By contrasting the different approaches taken by different nation states, and considering how closely they align with broad ‘consensus’ documents such as those produced by the European Commission, the OECD, and the World Health Organization, the intention is to develop a series of concrete policy recommendations that the authors believe will help achieve ‘successful’ development, deployment and use of AI in healthcare internationally.

By doing so, this project seeks to understand where the ambition for AI use in health systems matches up with reality. Given the barriers to regulating AI technology for direct patient care, we will also explore if and where health systems could invest in AI technologies (such as logistics, data processing, and operations) that don’t require rigorous regulator approval and pose less risk to patients.

Unibo involved Department/s:
Department of Legal Studies

Other Participant:
Fujitsu Limited


The research project focuses on the accountability of AI with the goals of landscaping existing legal and ethical problems and solutions and developing conceptual and normative analyses to address them. As a specific grounding, accountable AI will be discussed in the context of financial services.

Artificial Intelligence (AI) provides huge opportunities to improve private and public life, as well as our environment (consider the development of smart cities or the problems caused by carbon emissions). Unfortunately, such opportunities are also coupled to significant ethical challenges. The growing reliance on AI to shape choices and to make decisions, and the gradual reduction of human involvement or even oversight over many automatic processes, pose pressing issues of fairness, accountability and respect for human rights, particularly privacy.

Ethical analyses of AI can help the private and public sector to meet these challenges. Ethics of AI spans a wide range of topics, from the attribution of responsibility for AI mistakes and failures and the explanation of AI processes to the definition of the principles guiding the deployment of AI in a variety of areas, e.g. security, defence, finance, healthcare, and marketing.

This project, funded by Fujitsu in collaboration with the Department of Legal Studies of the University of Bologna, and directed by Professor Luciano Floridi as Principal Investigator, will focus on the accountability of AI with the goals of landscaping existing legal and ethical problems and solutions and developing conceptual and normative analyses to address them. As a specific grounding of the project, accountable AI will be discussed in the context of financial services.

Given the rapidly growing use of AI, it is crucial to ensure that it will not lead to harmful, unintended consequences, especially when it involves sensitive decisions or tasks and excludes or even precludes human supervision. This project will focus on the accountability of AI services (with a focus on financial services). While there is a growing debate and an adequate level of understanding on the privacy harms due to AI, limited attention has been dedicated to addressing questions concerning accountability for AI-based decisions in the context of increasingly datafied and AI-driven contexts, such as financial markets. Filling these gaps will be the goal of this project, which will last 12 months, and consist of 2 work packages.

Unibo involved Department/s:
Department of Legal Studies


The 2008 financial crisis caused society to question the reliability of the banking system. Decentralised Finance - or, DeFi - was born to disrupt such a system by changing the way we invest, lend, borrow, and many other activities. The European Union (EU) is establishing the rules for a safe DeFi era (take their Proposal for a Regulation on Markets in Crypto-assets issued in 2020). Currently, the literature has identified two main concerns: crypto exchanges and stablecoins. I argue that smart contracts, low liquidity issues, and flash-loans should also be studied closely.

The aim of this project is to (i) provide a state-of-the art review of DeFi, (ii) understand the ongoing discussions on its regulation and (iii) establish the ground for further discussion. I will focus on the GELSI (governance, ethical, legal, social, implications) of DeFi, together with its risks and opportunities. This technology will change our lives, and thus it is imperative that it is understood, in order to prevent costly mistakes - both financial and social.

Unibo involved Department/s:
Department of Legal Studies


Today Open Source Intelligence (OSINT), i.e. information derived from publicly available sources, makes up between 80 and 90 per cent of all intelligence activities carried out by Law Enforcement Agencies (LEAs) and intelligence services in the West. Developments in the fields of data mining, machine learning, visual forensics and most importantly, the growing computing power available for commercial use, have allowed OSINT practitioners to speed up (and sometimes even automate) intelligence collection and analysis, obtaining faster and more accurate results. As cyberspace expands to accommodate our ever-increasing online presence, so does the pool of actionable OSINT. These developments raise important concerns in terms of governance, ethical, legal and social implications (GELSI). New and crucial oversight concerns emerge alongside standard privacy concerns, as some of the more advanced data analysis tools require little to no supervision. Therefore, this project will produce a literature review on the GELSI of AI-powered OSINT tools, highlighting potential gaps and suggesting new research directions.

Unibo involved Department/s:
Department of Legal Studies


While risk-assessment algorithms were introduced with the promise to reduce human bias in decision-making, these tools have been found to reproduce or exacerbate pre-existing social biases. This phenomenon has given rise in the literature to the term “algorithmic bias”. I argue that what is often referred to as algorithmic bias, can also be conceived as a mirror of pre-existing social disparities. Starting from this intuition, the main aim of my research is to understand, as an AI Policy Researcher, in which ways algorithmic bias stands, or can stand, as evidence of these disparities, with the aim to inform policy interventions to tackle their pre-existing social causes. I focus on the cases of credit, health, and pre-trial risk assessment. My main research question is: “How, if at all, can bias in risk-assessment tools work as evidence for policy?” Given the presence of unfair differences in the way risk-assessment algorithms perform towards different social groups, I enquire whether I can qualify and quantify these differences in a way that is informative for policymaking.