Extracting structured and grounded fact triples from raw text is a fundamental task in Information Extraction (IE). Existing IE datasets are typically collected from Wikipedia articles, using hyperlinks to link entities to the Wikidata knowledge base. However, models trained only on Wikipedia have limitations when applied to web domains, which often contain noisy text or text that does not have any factual information. We present WebIE, the first large-scale, entity-linked closed IE dataset consisting of 1.6M sentences automatically collected from the English Common Crawl corpus. WebIE also includes negative examples, i.e. sentences without fact triples, to better reflect the data on the web. We annotate ~21K triples from WebIE through crowdsourcing and introduce mWebIE, a translation of the annotated set in four other languages: French, Spanish, Portuguese, and Hindi. We evaluate the in-domain, out-of-domain, and zero-shot cross-lingual performance of generative IE models and find models trained on WebIE show better generalisability. We also propose three training strategies that use entity linking as an auxiliary task. Our experiments show that adding Entity-Linking objectives improves the faithfulness of our generative IE models.

The proliferation of global censorship has led to the development of a plethora of measurement platforms to monitor and expose it. Censorship of the domain name system (DNS) is a key mechanism used across different countries. It is currently detected by applying heuristics to samples of DNS queries and responses (probes) for specific destinations. These heuristics, however, are both platform-specific and have been found to be brittle when censors change their blocking behavior, necessitating a more reliable automated process for detecting censorship. In this paper, we explore how machine learning (ML) models can (1) help streamline the detection process, (2) improve the potential of using large-scale datasets for censorship detection, and (3) discover new censorship instances and blocking signatures missed by existing heuristic methods. Our study shows that supervised models, trained using expert-derived labels on instances of known anomalies and possible censorship, can learn the detection heuristics employed by different measurement platforms. More crucially, we find that unsupervised models, trained solely on uncensored instances, can identify new instances and variations of censorship missed by existing heuristics. Moreover, both methods demonstrate the capability to uncover a substantial number of new DNS blocking signatures, i.e., injected fake IP addresses overlooked by existing heuristics. These results are underpinned by an important methodological finding: comparing the outputs of models trained using the same probes but with labels arising from independent processes allows us to more reliably detect cases of censorship in the absence of ground-truth labels of censorship.

Synthetic data generation is a promising solution to address privacy issues with the distribution of sensitive health data. Recently, diffusion models have set new standards for generative models for different data modalities. Also very recently, structured state space models emerged as a powerful modeling paradigm to capture long-term dependencies in time series. We put forward SSSD-ECG, as the combination of these two technologies, for the generation of synthetic 12-lead electrocardiograms conditioned on more than 70 ECG statements. Due to a lack of reliable baselines, we also propose conditional variants of two state-of-the-art unconditional generative models. We thoroughly evaluate the quality of the generated samples, by evaluating pretrained classifiers on the generated data and by evaluating the performance of a classifier trained only on synthetic data, where SSSD-ECG clearly outperforms its GAN-based competitors. We demonstrate the soundness of our approach through further experiments, including conditional class interpolation and a clinical Turing test demonstrating the high quality of the SSSD-ECG samples across a wide range of conditions.

With the introduction of machine learning in high-stakes decision making, ensuring algorithmic fairness has become an increasingly important problem to solve. In response to this, many mathematical definitions of fairness have been proposed, and a variety of optimisation techniques have been developed, all designed to maximise a defined notion of fairness. However, fair solutions are reliant on the quality of the training data, and can be highly sensitive to noise. Recent studies have shown that robustness (the ability for a model to perform well on unseen data) plays a significant role in the type of strategy that should be used when approaching a new problem and, hence, measuring the robustness of these strategies has become a fundamental problem. In this work, we therefore propose a new criterion to measure the robustness of various fairness optimisation strategies - the robustness ratio. We conduct multiple extensive experiments on five bench mark fairness data sets using three of the most popular fairness strategies with respect to four of the most popular definitions of fairness. Our experiments empirically show that fairness methods that rely on threshold optimisation are very sensitive to noise in all the evaluated data sets, despite mostly outperforming other methods. This is in contrast to the other two methods, which are less fair for low noise scenarios but fairer for high noise ones. To the best of our knowledge, we are the first to quantitatively evaluate the robustness of fairness optimisation strategies. This can potentially can serve as a guideline in choosing the most suitable fairness strategy for various data sets.

In recent years, machine learning (ML) has relied heavily on crowdworkers both for building datasets and for addressing research questions requiring human interaction or judgment. The diverse tasks performed and uses of the data produced render it difficult to determine when crowdworkers are best thought of as workers (versus human subjects). These difficulties are compounded by conflicting policies, with some institutions and researchers regarding all ML crowdworkers as human subjects and others holding that they rarely constitute human subjects. Notably few ML papers involving crowdwork mention IRB oversight, raising the prospect of non-compliance with ethical and regulatory requirements. We investigate the appropriate designation of ML crowdsourcing studies, focusing our inquiry on natural language processing to expose unique challenges for research oversight. Crucially, under the U.S. Common Rule, these judgments hinge on determinations of aboutness, concerning both whom (or what) the collected data is about and whom (or what) the analysis is about. We highlight two challenges posed by ML: the same set of workers can serve multiple roles and provide many sorts of information; and ML research tends to embrace a dynamic workflow, where research questions are seldom stated ex ante and data sharing opens the door for future studies to aim questions at different targets. Our analysis exposes a potential loophole in the Common Rule, where researchers can elude research ethics oversight by splitting data collection and analysis into distinct studies. Finally, we offer several policy recommendations to address these concerns.

Politicians think they have a way to hold companies accountable for troublesome generative AI: take away their legal protection. Senators Richard Blumenthal and Josh Hawley have introduced a No Section 230 Immunity for AI Act that, as the name suggests, would prevent OpenAI, Google and similar firms from using the Communications Decency Act's Section 230 to waive liability for harmful content and avoid lawsuits. If someone created a deepfake image or sound bite to ruin a reputation, for instance, the tool developer could be held responsible alongside the person who used it. Hawley characterizes the bill as forcing AI creators to "take responsibility for business decisions" as they're developing products. He also casts the legislation as a "first step" toward creating rules for AI and establishing safety measures.

During Prime Minister Rishi Sunak's recent visit to Washington D.C., as he announced that the U.K. would host the first global summit on AI regulation later this year, he bristled in response to a reporter's question about whether the "midsize country" could naturally lead the debate, given that the E.U. is close to passing a landmark AI bill. "That midsize country happens to be a global leader in AI," he said. "You would be hard-pressed to find many other countries other than the U.S. in the Western world with more expertise and talent in AI." The Prime Minister's response revealed the dilemma he now faces in positioning the U.K. as a key player in reining in AI's potential negative consequences without stifling innovation, amid growing fears around generative artificial intelligence. Following the U.K.'s departure from the European Union, experts say Sunak is attempting to carve out a pivotal role to help keep the country globally relevant by playing the role of an "honest broker" between the different regulatory approaches of the E.U. and the U.S. when it comes to AI.

Meanwhile, efforts are progressing slowly in the United States, where Congress has not passed a federal online privacy bill or other comprehensive legislation regulating social media. On Tuesday, Schumer hosted the first of three private AI briefings for lawmakers. MIT professor Antonio Torralba, who specializes in computer vision and machine learning, was scheduled to brief lawmakers on "Where is AI Today," covering where AI is deployed and what it's currently capable of. The next session will look at the future of AI and how it could evolve over the next decade, and the third, classified session will cover how the military and intelligence community currently uses AI.

The EU has taken a major step towards passing one of the world's first laws governing artificial intelligence after its main legislative branch approved the text of draft legislation that includes a blanket ban on police use of live facial recognition technology in public places. The European parliament approved rules aimed at setting a global standard for the technology, which encompasses everything from automated medical diagnoses to some types of drone, AI-generated videos known as deepfakes, and bots such as ChatGPT. MEPs will now thrash out details with EU countries before the draft rules become legislation. "AI raises a lot of questions socially, ethically, economically. But now is not the time to hit any'pause button'. On the contrary, it is about acting fast and taking responsibility," said Thierry Breton, the European commissioner for the internal market.

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