Statutory article retrieval (SAR), the task of retrieving statute law articles relevant to a legal question, is a promising application of legal text processing. In particular, high-quality SAR systems can improve the work efficiency of legal professionals and provide basic legal assistance to citizens in need at no cost. Unlike traditional ad-hoc information retrieval, where each document is considered a complete source of information, SAR deals with texts whose full sense depends on complementary information from the topological organization of statute law. While existing works ignore these domain-specific dependencies, we propose a novel graph-augmented dense statute retriever (G-DSR) model that incorporates the structure of legislation via a graph neural network to improve dense retrieval performance. Experimental results show that our approach outperforms strong retrieval baselines on a real-world expert-annotated SAR dataset.

Artificial Intelligence (AI) is the ability of machines to perform tasks that would normally require human intelligence. The rise in computer power, along with digital data, is what makes AI important today. Although forms of AI have existed since 1956, the next phase of artificial intelligence is only recently visible[1]. A recent report by UNESCO estimates that artificial intelligence research grew 50% from 2015 to 2019. The combined research growth, along with an increase in spending, is creating a technological land rush in the field of artificial intelligence. The World Intellectual Property Organization has highlighted AI as one of the most rapidly growing areas of issued and filed patent applications.

Well, a lot has happened since I first started looking into the "World's First Robot Lawyer," from DoNotPay. First, Joshua Browder, DoNotPay's CEO, reached out to me via direct message (DM) and told me he would get me access to my documents by 2 PM the next day – Tuesday, January 24th – saying that the delay was caused by my account being locked for "inauthentic activity," a term he did not explain or define. Then, Josh claimed he was going to pull out of the industry entirely, canceling his courtroom stunt and saying he would disable all the legal tools on DoNotPay.com. He said he was doing it because it was a distraction, but the fact that he cited exactly the same two documents that I was waiting to receive seemed like a hell of a coincidence. But plus ça change, plus c'est la même fucking chose, as the poet says.

There has been concern within the artificial intelligence (AI) community and the broader society regarding the potential lack of fairness of AI-based decision-making systems. Surprisingly, there is little work quantifying and guaranteeing fairness in the presence of uncertainty which is prevalent in many socially sensitive applications, ranging from marketing analytics to actuarial analysis and recidivism prediction instruments. To this end, we study a longitudinal censored learning problem subject to fairness constraints, where we require that algorithmic decisions made do not affect certain individuals or social groups negatively in the presence of uncertainty on class label due to censorship. We argue that this formulation has a broader applicability to practical scenarios concerning fairness. We show how the newly devised fairness notions involving censored information and the general framework for fair predictions in the presence of censorship allow us to measure and mitigate discrimination under uncertainty that bridges the gap with real-world applications. Empirical evaluations on real-world discriminated datasets with censorship demonstrate the practicality of our approach.

GDL is a subfield of deep learning (Goodfellow et al., Reference Goodfellow, Bengio and Courville2016) with a focus on generation of new data. Following the definition provided by Foster (Reference Foster2019), a generative model describes how a dataset is generated (in terms of a probabilistic model); by sampling from this model, we are able to generate new data. Nowadays, machine-generated artworks have entered the market (Vernier et al., Reference Vernier, Caselles-Dupré and Fautrel2020), they are fully accessible online,Footnote 1 and they have the focus of major investments.Footnote 2 Ethical debates have, fortunately, found a place in the conversation (for an interesting summary of machine learning researches related to fairness, see Chouldechova and Roth (Reference Chouldechova and Roth2020)) because of biases and discrimination they may cause (as happened with AI Portrait Ars [O'Leary, Reference O'Leary2019], leading to some very remarkable attempts to overcome them, as in Xu et al. (Reference Xu, Yuan, Zhang and Wu2018) or Yu et al. (Reference Yu, Li, Zhou, Malik, Davis and Fritz2020)). In this context, it is possible to identify at least three problems: the use of protected works, which have to be stored in memory until the end of the training process (even if not for more time, in order to verify and reproduce the experiment); the use of protected works as training set, processed by deep learning techniques through the extraction of information and the creation of a model upon them; and the ownership of intellectual property (IP) rights (if a rightholder would exist) over the generated works. Although these arguments have already been extensively studied (e.g., Sobel (Reference Sobel2017) examines use as training set and Deltorn and Macrez (Reference Deltorn and Macrez2018) discuss authorship), this paper aims at analyzing all the problems jointly, creating a general overview useful for both the sides of the argument (developers and policymakers); aims at focusing only on GDL, which (as we will see) has its own peculiarities, and not on artificial intelligence (AI) in general (which contains too many different subfields that cannot be generalized as a whole); and is written by GDL researchers, which may help provide a new and practical perspective to the topic.

FBI Director Christopher Wray said Thursday that he was "deeply concerned" about the Chinese government's artificial intelligence program, asserting that it was "not constrained by the rule of law." Speaking during a panel session at the World Economic Forum in Davos, Switzerland, Wray said Beijing's AI ambitions were "built on top of massive troves of intellectual property and sensitive data that they've stolen over the years." He said that left unchecked, China could use artificial intelligence advancements to further its hacking operations, intellectual property theft and repression of dissidents inside the country and beyond. "That's something we're deeply concerned about. I think everyone here should be deeply concerned about," he said.

Hey, party people, it's Kyle, continuing to step in for Greg to write Week in Review as he spends time with his newborn. Dunno about y'all, but it's been a week. But because the news never sleeps, I'm rallying with the help of a fourth cup of coffee. I've talked your ears off about it at this point, but I'm under contractual obligation (not really, but still) to mention TechCrunch's upcoming Early Stage 2023 event in Boston on April 20. The one-day summit on startups will include advice and takeaways from top experts, plus opportunities to meet fellow founders and share your own entrepreneurial experiences.

In a strange way, the best thing that could have happened to Google (now masquerading as Alphabet, its parent company) was Facebook. Because although Google invented surveillance capitalism, arguably the most toxic business model since the opium trade, it was Facebook that got into the most trouble for its abuses of it. The result was that Google enjoyed an easier ride. Naturally, it had the odd bit of unpleasantness with the EU, with annoying fines and long drawn out legal wrangles. But it was the Facebook boss, Mark Zuckerberg – not Google's Larry Page, Sergey Brin and their adult supervisor Eric Schmidt – who was awarded the title of evil emperor of the online world.

When you run a major app, all it takes is one mistake to put countless people at risk. Such is the case with Diksha, a public education app run by India's Ministry of Education that exposed the personal information of around 1 million teachers and millions of students across the country. The data, which included things like full names, email addresses, and phone numbers, was publicly accessible for at least a year and likely longer, potentially exposing those impacted to phishing attacks and other scams. Speaking of cybercrime, the LockBit ransomware gang has long operated under the radar, thanks to its professional operation and choice of targets. But over the past year, a series of missteps and drama have thrust it into the spotlight, potentially threatening its ability to continue operating with impunity.

We demonstrate a proof-of-concept of a large language model conducting corporate lobbying related activities. An autoregressive large language model (OpenAI's text-davinci-003) determines if proposed U.S. Congressional bills are relevant to specific public companies and provides explanations and confidence levels. For the bills the model deems as relevant, the model drafts a letter to the sponsor of the bill in an attempt to persuade the congressperson to make changes to the proposed legislation. We use hundreds of novel ground-truth labels of the relevance of a bill to a company to benchmark the performance of the model. It outperforms the baseline of predicting the most common outcome of irrelevance. We also benchmark the performance of the previous OpenAI GPT-3 model (text-davinci-002), which was the state-of-the-art model on many academic natural language tasks until text-davinci-003 was recently released. The performance of text-davinci-002 is worse than the simple baseline. Longer-term, if AI begins to influence law in a manner that is not a direct extension of human intentions, this threatens the critical role that law as information could play in aligning AI with humans. Initially, AI is being used to simply augment human lobbyists for a small portion of their daily tasks. However, firms have an incentive to use less and less human oversight over automated assessments of policy ideas and the written communication to regulatory agencies and Congressional staffers. The core question raised is where to draw the line between human-driven and AI-driven policy influence.