Standard federated learning (FL) approaches are vulnerable to the free-rider dilemma: participating agents can contribute little to nothing yet receive a well-trained aggregated model.

In this work, we study the relationship between these objectives.

In this article, we continue this line of investigation.

Given a map description through global traversal navigation instructions (e.g., visiting each room sequentially with action signals such as north, west, etc.), an LLM can often infer the implicit spatial layout of the environment and answer user queries by providing a shortest path from a start to a destination (for instance, navigating from the lobby to a meeting room via the hall and elevator). However, such context-dependent querying becomes incapable as the environment grows much longer, motivating the need for incremental map construction that builds a complete topological graph from stepwise observations. We propose a framework for LLM-driven construction and map repair, designed to detect, localize, and correct structural inconsistencies in incrementally constructed navigation graphs. Central to our method is the Version Control, which records the full history of graph edits and their source observations, enabling fine-grained rollback, conflict tracing, and repair evaluation. We further introduce an Edge Impact Score to prioritize minimal-cost repairs based on structural reachability, path usage, and conflict propagation. To properly evaluate our approach, we create a refined version of the MANGO benchmark dataset by systematically removing non-topological actions and inherent structural conflicts, providing a cleaner testbed for LLM-driven construction and map repair. Our approach significantly improves map correctness and robustness, especially in scenarios with entangled or chained inconsistencies. Our results highlight the importance of introspective, history-aware repair mechanisms for maintaining coherent spatial memory in LLM agents.

Whether and how to regulate AI is one of the defining questions of our times - a question that is being debated locally, nationally, and internationally. We argue that much of this debate is proceeding on a false premise. Specifically, our article challenges the prevailing academic consensus that the European Union's AI regulatory framework is fundamentally rights-driven and the correlative presumption that other rights-regarding nations should therefore follow Europe's lead in AI regulation. Rather than taking rights language in EU rules and regulations at face value, we show how EU AI regulation is the logical outgrowth of a particular cultural, political, and historical context. We show that although instruments like the General Data Protection Regulation (GDPR) and the AI Act invoke the language of fundamental rights, these rights are instrumentalized - used as rhetorical cover for governance tools that address systemic risks and maintain institutional stability. As such, we reject claims that the EU's regulatory framework and the substance of its rules should be adopted as universal imperatives and transplanted to other liberal democracies. To add weight to our argument from historical context, we conduct a comparative analysis of AI regulation in five contested domains: data privacy, cybersecurity, healthcare, labor, and misinformation. This EU-US comparison shows that the EU's regulatory architecture is not meaningfully rights-based. Our article's key intervention in AI policy debates is not to suggest that the current American regulatory model is necessarily preferable but that the presumed legitimacy of the EU's AI regulatory approach must be abandoned.

Despite over a decade of legislative efforts to address modern slavery in the supply chains of large corporations, the effectiveness of government oversight remains hampered by the challenge of scrutinizing thousands of statements annually. While Large Language Models (LLMs) can be considered a well established solution for the automatic analysis and summarization of documents, recognizing concrete modern slavery countermeasures taken by companies and differentiating those from vague claims remains a challenging task. To help evaluate and fine-tune LLMs for the assessment of corporate statements, we introduce a dataset composed of 5,731 modern slavery statements taken from the Australian Modern Slavery Register and annotated at the sentence level. This paper details the construction steps for the dataset that include the careful design of annotation specifications, the selection and preprocessing of statements, and the creation of high-quality annotation subsets for effective model evaluations. To demonstrate our dataset's utility, we propose a machine learning methodology for the detection of sentences relevant to mandatory reporting requirements set by the Australian Modern Slavery Act. We then follow this methodology to benchmark modern language models under zero-shot and supervised learning settings.

This story originally appeared in WIRED Italia and has been translated from Italian. The latest in a series of duels announced by the European Commission is with Bing, Microsoft's search engine. Brussels suspects that the giant based in Redmond, Washington, has failed to properly moderate content produced by the generative AI systems on Bing, Copilot, and Image Creator, and that as a result, it may have violated the Digital Services Act (DSA), one of Europe's latest digital regulations. On May 17, the EU summit requested company documents to understand how Microsoft handled the spread of hallucinations (inaccurate or nonsensical answers produced by AI), deepfakes, and attempts to improperly influence the upcoming European Parliament elections. At the beginning of June, voters in the 27 states of the European Union will choose their representatives to the European Parliament, in a campaign over which looms the ominous shadow of technology with its potential to manipulate the outcome. The commission has given Microsoft until May 27 to respond, only days before voters go to the polls.

Artificial intelligence is creeping into every aspect of our lives. AI-powered software is triaging hospital patients to determine who gets which treatment, deciding whether an asylum seeker is lying or telling the truth in their application and even conjuring up weird conceits for sitcoms. Just lately, these kinds of tools have been helping killer robots select their targets in the war in Ukraine. AI systems have been proven to carry systemic biases again and again, but their increasing centrality to the way we live makes those debates even more urgent. In typical tech fashion, AI-driven tools are advancing much faster than the laws that could theoretically govern them.

Ethical AI will need careful planting of many ecosystems. Ethical AI has been a concern of AI leaders, and practitioners for many years, but finally it seems, global jurisdictions are starting to move from policy formulation and stakeholder engagement to putting some teeth into drafting legal bills or acts. Expect many new laws to pass in 2023, tightening up citizen privacy and creating risk frameworks and audit requirements for data bias, privacy and security risks. At the same time, regulators are going to have to evolve an entire global ecosystem to ensure AI audits are effectively conducted and many questions loom as to who will validate certifications for AI audit practices and will we over burden AI innovations like we have done in so many other regulated operating practices that the risk and costs of non-conformance inhibit's innovation and capital funding? Finding a balance will be key.