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Gianclaudio Malgieri

Publications and source records attributed to Gianclaudio Malgieri.

3 recordsLinked to original sources

Click, Branch, Audit: A Decision-Tree Toolkit for Assessing Fundamental Rights Impacts under the Digital Services Act of Very Large Platforms & Search Engines

The EU Digital Services Act (DSA) requires Very Large Online Platforms and Search Engines (VLOPs/VLOSEs) to assess and mitigate systemic risks, including actual or foreseeable negative effects on fundamental rights, and subjects these assessments to independent audit. Yet how such rights-relevant risks should be identified, assessed, evidenced, and documented remains methodologically under-specified. We address this gap by introducing a decision-tree-based toolkit that operationalizes Article 34(1)(b) DSA and related audit requirements into a structured assessment process. The toolkit decomposes platforms as systems-of-systems and guides assessors through DSA applicability, context definition, subsystem and stakeholder identification, risk identification, risk assessment, reporting, and visualization. A central design contribution is its treatment of vulnerability as situational and relational: rather than assigning vulnerability to fixed groups, the toolkit asks how particular platform subsystems and dependencies may place stakeholders in vulnerable positions. It further makes fundamental rights-relevant judgments traceable by recording the affected right, interference and justification analysis within the structure of a proportionality test, while also covering a perception and impact dimension including severity, scope, duration, reversibility, as well as practical consequences. We developed the toolkit iteratively through a design-science process combining literature-derived design requirements, three interdisciplinary expert workshops (N=4; N=11; N=20) testing the framework design with two DSA user persona scenarios. We contribute an extensible methodological foundation for making fundamental-rights risk assessments under the DSA more streamlined, systematic, transparent, and auditable.

cs.CY↗

Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders.

cs.AI↗

Queering the ethics of AI

This book chapter delves into the pressing need to "queer" the ethics of AI to challenge and re-evaluate the normative suppositions and values that underlie AI systems. The chapter emphasizes the ethical concerns surrounding the potential for AI to perpetuate discrimination, including binarism, and amplify existing inequalities due to the lack of representative datasets and the affordances and constraints depending on technology readiness. The chapter argues that a critical examination of the neoliberal conception of equality that often underpins non-discrimination law is necessary and cannot stress more the need to create alternative interdisciplinary approaches that consider the complex and intersecting factors that shape individuals' experiences of discrimination. By exploring such approaches centering on intersectionality and vulnerability-informed design, the chapter contends that designers and developers can create more ethical AI systems that are inclusive, equitable, and responsive to the needs and experiences of all individuals and communities, particularly those who are most vulnerable to discrimination and harm.

cs.HC↗