The European Union's Artificial Intelligence Act (AI Act), finalized in February 2024, mandates comprehensive transparency and explainability requirements for AI systems to enable effective oversight and safeguard fundamental rights. However, the practical implementation of these requirements faces challenges due to tensions between the need for meaningful explanations and the potential risks to intellectual property and commercial interests of AI providers. This research proposes the Transparency-Explainability Functionality and Tensions (TEFT) framework to systematically analyze the complex interplay of legal, technical, and socio-ethical factors shaping the realization of algorithmic transparency and explainability in the EU context. Through a two-pronged approach combining a focused literature review and an in-depth examination of the AI Act's provisions, we identify key friction points and challenges in operationalizing the right to explanation. The TEFT framework maps the interests and incentives of various stakeholders, including AI providers & deployers, oversight bodies, and affected individuals, while considering their goals, expected benefits, risks, possible negative impacts, and context to algorithmic explainability.

While machine learning shows promise in automated knowledge generation, current techniques such as large language models and micro-targeted influence operations can be exploited for harmful purposes like the proliferation of disinformation. The European Union’s Digital Services Act (DSA) is an exemplary policy response addressing these harms generated by online platforms. In this regard, it necessitates a comprehensive evaluation of its impact on curbing the harmful downstream effects of these opaque practices. Despite their harmful applications, we argue that machine learning techniques offer immense, yet under-exploited, potential for unraveling the impacts of regulations like the DSA. Following an analysis that reveals possible limitations in the DSA’s provisions, we call for resolute efforts to address methodological barriers around appropriate data access, isolating marginal regulatory effects, and facilitating generalization across different contexts. Given the identified advantages of data-driven approaches to regulatory delivery, we advocate for machine learning research to help quantify the policy impacts on online harms.

This research project aims to develop and validate explanatory facilities to enhance information reception of process mining solutions, which could inform and be translated to other business intelligence platforms. Process mining, a nascent field for analyzing event data stored in information systems, faces challenges in adoption, engagement, and comprehensive explainability frameworks. The research problem lies in the difficulties organizations face when understanding the return on investment and integration requirements associated with process mining operationalization. Furthermore, users often struggle to comprehend the elaboration and representation of process outputs. This issue is compounded by the limited application of Explainable AI (XAI) in process mining, which so far has been predominantly focused on prediction and monitoring activities without a holistic view of explainability trade-offs.

With increasing digitalization, Artificial Intelligence (AI) is becoming ubiquitous. AI-based systems to identify, optimize, automate, and scale solutions to complex economic and societal problems are being proposed and implemented. This has motivated regulation efforts, including the Proposal of an EU AI Act. This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them, focusing on (but not limited to) the Proposal. We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives. Then, we map these perspectives along three axes of interests: (i) Standardization vs. Localization, (ii) Utilitarianism vs. Egalitarianism, and (iii) Consequential vs. Deontological ethics which leads us to identify a pattern of common arguments and tensions between these axes. Positioning the discussion within the axes of interest and with a focus on reconciling the key tensions, we identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.