AI systems are increasingly embedded in workplace platforms, where they infer performance, attention, and affect from everyday traces. The EU AI Act regulates such systems through risk categories and governance concepts, yet it remains unclear how these concepts operationalize when AI becomes an agentic infrastructure. We present a near-future performance review fiction accompanied by short legal annotations. Rather than assessing compliance, the annotations function as legal exegesis: They mark where oversight, contestability, and sensitive inference shift meaning when enacted through interfaces, procedures, and cross-referencing agents. The provocation foregrounds three dynamics: The chameleonic aspects of risk categories, oversight collapsing into recursive system input, and sensitive domains becoming inferable through proxy data traces. We argue that "fiction and exegesis"offers a situated method for interrogating legal governance before such systems stabilize in practice.

Illegal content reporting mechanisms are a key technical and organizational measure through which online platforms address the dissemination of illegal content under European Union law. Under the Digital Services Act (DSA), user notices submitted pursuant to Article 16 must be sufficiently substantiated and provided in good faith, requiring users to interpret legal and procedural language and translate it into legally meaningful categories and reasons. In practice, however, reporting illegal content remains cumbersome across major social media platforms, placing substantial cognitive and legal demands on users. Without effective support at the reporting interface, operationalizing Article 16 in practice remains challenging. We investigate how large language model (LLM)-based assistants can support illegal content reporting. In a controlled user study (N = 450) using an interface modeled on a major platform's reporting workflow, we compare three conditions: (1) a conventional explainable AI assistant (XAI) that suggests a single legal category with a rationale, (2) an evaluative AI assistant (EvalAI) that presents balanced pro and con arguments across candidate legal provisions for user deliberation, and (3) a baseline reflecting unaided reporting (Baseline). We further examine these assistance forms under systematically varied AI error regimes. Our results show that EvalAI improves provision-level accuracy under AI error regimes and reduces misclassification distance relative to conventional XAI, particularly for near-miss and overbreadth errors. In contrast, conventional XAI does not improve - and can degrade - the quality of users' rationales relative to unaided reporting, despite enabling faster decisions when the AI output is correct. We discuss implications for the design of compliance-oriented reporting interfaces, highlighting trade-offs between accuracy, deliberation, and vulnerability to misleading AI output.

More people voted in 2024 than any other year in human history, while often relying on the internet for political information. This combination resulted in critical challenges for democracy. To address these concerns, we designed an exhibition that applied interactive experiences to help visitors understand the impact of digitization on democracy. This late-breaking work addresses the research questions: 1) What do participants, exposed to playful interventions, think about these topics? and 2) How do people estimate their skills and knowledge about countering misinformation? We collected data in 5 countries through showcases held within weeks of relevant 2024 elections. During visits, participants completed a survey detailing their experiences and emotional responses. Participants expressed high levels of self-confidence regarding the detection of misinformation and spotting AI-generated content. This paper contributes to addressing digital literacy needs by fostering engaging interactions with AI and politically relevant issues surrounding campaigning and misinformation.

The rise of digital platforms has transformed political campaigning, introducing complex regulatory challenges. This paper presents a comprehensive taxonomy for analyzing political content in the EU’s digital electoral landscape, aligning with the requirements set forth in new regulations, such as the Digital Services Act (DSA). Using a legal doctrinal methodology, we construct a detailed codebook that enables systematic content analysis across user-generated and political ad content to assess compliance with regulatory mandates.

Platforms have a problem with harmful or illegal content online. Flagging, which is an empowering tool for users to report violating content. A new European Union law, the Digital Services Act (DSA), seeks to harmonize the regulation of the flagging process. This paper examines how these flagging mechanisms support user action through semi-structured interviews (N=12) with regulatory authorities and professional reporting experts, using a walkthrough approach (with case studies based on flagging systems on Facebook and TikTok). We found tensions between the empowerment of users with additional reporting options and how it burdens users within service interfaces and processes; users need to understand the law, participate in a legal process, and differentiate between legal options and terms of service. Design choices, like the length of necessary reporting steps, also impacted expectations on the transparency of the reporting process. We close with design insights on support for users and stakeholders in the reporting process.

The Digital Services Act (DSA) represents a landmark legislative framework in the European Union, aimed at regulating online platforms, enhancing transparency, and mitigating systemic risks associated with digital services. The Act aligns with broader EU regulatory efforts, including the General Data Protection Regulation (GDPR) and the Artificial Intelligence (AI) Act, positioning it as a cornerstone of digital governance. A key objective is to create a harmonized internal market that prevents regulatory fragmentation while ensuring consumer protection and fundamental rights. The DSA introduces obligations for intermediary services, including very large online platforms (VLOPs) and very large online search engines (VLOSEs). Moreover, the regulation mandates due diligence measures such as transparency reporting, algorithmic accountability, and user rights protections. Transparency mechanisms include the publication of terms and conditions databases, the Statement of Reasons repository, and advertising libraries. Moreover, the DSA enforces structured risk assessment and mitigation strategies, particularly for systemic risks such as illegal content dissemination, disinformation, and fundamental rights violations. A core component of the DSA is its approach to content moderation, introducing user empowerment mechanisms such as Trusted Flaggers, internal complaint-handling systems, and out-of-court dispute resolution bodies. Additionally, the Regulation includes crisis response provisions enabling swift intervention by the European Commission in extraordinary circumstances. To ensure compliance, the DSA establishes independent audit requirements and risk-based oversight mechanisms, reinforcing platform accountability. This Chapter aims to give an overview and comprehensive introduction to these provisions.

Content moderation is a vital condition that online platforms must facilitate, according to the law, to create suitable online environments for their users. By the law, we mean national or European laws that require the removal of content by online platforms, such as EU Regulation 2021/784, which addresses the dissemination of terrorist content online. Content moderation required by these national or European laws, summarised here as ‘the law’, is different from the moderation of pieces of content that is not directly required by law but instead is conducted voluntarily by the platforms. New regulatory requests create an additional layer of complexity of legal grounds for the moderation of content and are relevant to platforms’ daily decisions. The decisions made are either grounded in reasons stemming from different sources of law, such as international or national provisions, or can be based on contractual grounds, such as the platform's Terms of Service and Community Standards. However, how to empirically measure these essential aspects of content moderation remains unclear. Therefore, we ask the following research question: How do online platforms interpret the law when they moderate online content? To understand this complex interplay and empirically test the quality of a platform's content moderation claims, this article develops a methodology that facilitates empirical evidence of the individual decisions taken per piece of content while highlighting the subjective element of content classification by human moderators. We then apply this methodology to a single empirical case, an anonymous medium-sized German platform that provided us access to their content moderation decisions. With more knowledge of how platforms interpret the law, we can better understand the complex nature of content moderation, its regulation and compliance practices, and to what degree legal moderation might differ from moderation due to contractual reasons in dimensions such as the need for context, information, and time. Our results show considerable divergence between the platform's interpretation of the law and ours. We believe that a significant number of platform legal interpretations are incorrect due to divergent interpretations of the law and that platforms are removing legal content that they falsely believe to be illegal (‘overblocking’) while simultaneously not moderating illegal content (‘underblocking’). In conclusion, we provide recommendations for content moderation system design that takes (legal) human content moderation into account and creates new methodological ways to test its quality and effect on speech in online platforms.

Large Language Models (LLMs) are expected to significantly impact various socio-technical systems, offering transformative possibilities for improved interaction between humans and technology. However, their integration poses complex challenges due to the intricate interplay between societal structures, human behaviour, and technological innovation. This research explores these multifaceted challenges, emphasising the need for a human-centered approach in integrating LLMs to ensure that technological advancements are aligned with ethical standards and societal needs. Utilizing a structured methodology comprising a workshop, literature analysis, and expert collaborations, the study uses a multi-dimensional human-centered AI framework to guide the responsible integration of LLMs. Key insights include the importance of inclusive data, considering unintended consequences, maintaining privacy, and respecting intellectual property rights. The paper identifies and advocates for principles like human-in-the-loop, continuous longitudinal studies, proactive awareness campaigns, and regular audits to develop LLMs that are ethically sound, adaptable, and effectively integrated into various socio-technical systems, thus addressing user needs and broader societal impacts. The paper also underlines the importance of collaboration among academia, industry, and policymakers to develop LLMs that are ethically aligned, socially beneficial, and adaptable to future societal needs. The findings offer valuable insights into the strategic integration of LLMs, advocating for a broader research perspective beyond industrial motivations to fully understand and leverage LLMs in socio-technical landscapes.

Safeguarding democratic elections is hard. Social media plays a vital role in the discourse around elections and during electoral campaigns. The following article provides an analysis of the 'systemic electoral risks' created by Twitter and Facebook and the mitigation strategies employed by the platforms. It is based on the 2020 proposal by the European Commission for the new Digital Services Act (DSA) in the context of the 2021 German federal elections. This article focuses on Twitter and Facebook and their roles during the German federal elections that took place on 26 September 2021. We analysed three systemic electoral risk categories: 1) the dissemination of illegal content, 2) negative effects on electoral rights, and 3) the influence of disinformation and developed systematic categories for this purpose. In conclusion, we discuss how to respond to these challenges as well as avenues for future research.

The AI Act represents a significant legislative effort by the European Union to govern the use of AI systems according to different risk-related classes, linking varying degrees of compliance obligations to the system's classification. However, it is often critiqued due to the lack of general public comprehension and effectiveness regarding the classification of AI systems to the corresponding risk classes. To mitigate those shortcomings, we propose a Decision-Tree-based framework aimed at increasing robustness, legal compliance and classification clarity with the Regulation. Quantitative evaluation shows that our framework is especially useful to individuals without a legal background, allowing them to improve considerably the accuracy and significantly reduce the time of case classification.