Interpreting the meaning of legal open-textured terms is a key task of legal professionals. An important source for this interpretation is how the term was applied in previous court cases. In this paper, we evaluate the performance of GPT-4 in generating factually accurate, clear and relevant explanations of terms in legislation. We compare the performance of a baseline setup, where GPT-4 is directly asked to explain a legal term, to an augmented approach, where a legal information retrieval module is used to provide relevant context to the model, in the form of sentences from case law. We found that the direct application of GPT-4 yields explanations that appear to be of very high quality on their surface. However, detailed analysis uncovered limitations in terms of the factual accuracy of the explanations. Further, we found that the augmentation leads to improved quality, and appears to eliminate the issue of hallucination, where models invent incorrect statements. These findings open the door to the building of systems that can autonomously retrieve relevant sentences from case law and condense them into a useful explanation for legal scholars, educators or practicing lawyers alike.

Extreme classification (XC) involves predicting over large numbers of classes (thousands to millions), with real-world applications like news article classification and e-commerce product tagging. The zero-shot version of this task requires generalization to novel classes without additional supervision. In this paper, we develop SemSup-XC, a model that achieves state-of-the-art zero-shot and few-shot performance on three XC datasets derived from legal, e-commerce, and Wikipedia data. To develop SemSup-XC, we use automatically collected semantic class descriptions to represent classes and facilitate generalization through a novel hybrid matching module that matches input instances to class descriptions using a combination of semantic and lexical similarity. Trained with contrastive learning, SemSup-XC significantly outperforms baselines and establishes state-of-the-art performance on all three datasets considered, gaining up to 12 precision points on zero-shot and more than 10 precision points on one-shot tests, with similar gains for recall@10. Our ablation studies highlight the relative importance of our hybrid matching module and automatically collected class descriptions.

This work presents CounterNet, a novel end-to-end learning framework which integrates Machine Learning (ML) model training and the generation of corresponding counterfactual (CF) explanations into a single end-to-end pipeline. Counterfactual explanations offer a contrastive case, i.e., they attempt to find the smallest modification to the feature values of an instance that changes the prediction of the ML model on that instance to a predefined output. Prior techniques for generating CF explanations suffer from two major limitations: (i) all of them are post-hoc methods designed for use with proprietary ML models -- as a result, their procedure for generating CF explanations is uninformed by the training of the ML model, which leads to misalignment between model predictions and explanations; and (ii) most of them rely on solving separate time-intensive optimization problems to find CF explanations for each input data point (which negatively impacts their runtime). This work makes a novel departure from the prevalent post-hoc paradigm (of generating CF explanations) by presenting CounterNet, an end-to-end learning framework which integrates predictive model training and the generation of counterfactual (CF) explanations into a single pipeline. Unlike post-hoc methods, CounterNet enables the optimization of the CF explanation generation only once together with the predictive model. We adopt a block-wise coordinate descent procedure which helps in effectively training CounterNet's network. Our extensive experiments on multiple real-world datasets show that CounterNet generates high-quality predictions, and consistently achieves 100% CF validity and low proximity scores (thereby achieving a well-balanced cost-invalidity trade-off) for any new input instance, and runs 3X faster than existing state-of-the-art baselines.

On April 3, 2001, Alanis Morissette and Don Henley appeared before Congress in a bid to save the music industry. Henley, the drummer and a lead vocalist for the Eagles, was dressed in a pin-striped suit. Morissette, the Grammy Award-winning singer of "You Oughta Know," wore a red top and a purple ring. Also present was Hilary Rosen, the president and C.E.O. of the Recording Industry Association of America (R.I.A.A.); Shawn Fanning, the co-founder of Napster; Ken Berry, the president and C.E.O. of EMI Recorded Music; and Dianne Feinstein, the then sixty-seven-year-old senator from California. The Senate Judiciary Committee had called the hearing because online file sharing was understood to be threatening the viability of the entire music industry, and of the future of art in America. As the sole musicians to testify, Morissette and Henley might have chosen to echo the chorus of their record-industry colleagues, bemoaning piracy and praising the R.I.A.A.'s moves to stop it.

The United States Federal Trade Commission has accused Amazon.com of enrolling millions of consumers into its paid subscription Amazon Prime service without their consent and making it hard for them to cancel, the latest action by the agency against the e-commerce giant in recent weeks. The FTC sued in Amazon in federal court in Seattle on Wednesday, alleging that "Amazon has knowingly duped millions of consumers into unknowingly enrolling in Amazon Prime." The FTC said Amazon used "manipulative, coercive or deceptive user-interface designs known as'dark patterns' to trick consumers into enrolling in automatically renewing Prime subscriptions." The lawsuit is one of several actions taken by President Joe Biden's administration intended to rein in the outsized market power of Big Tech firms as it tries to increase competition to create greater consumer protection. The FTC said Amazon Prime is the world's largest subscription programme, generating $25bn in revenue annually.

Calling the rapid growth of artificial intelligence tools a "moment of revolution," Senate Majority Leader Chuck Schumer said Wednesday that the government must act quickly to regulate companies that are developing it. The New York Democrat said he is working on what he calls "exceedingly ambitious" bipartisan legislation to maximize the technology's benefits and mitigate significant risks. While Schumer did not lay out details of such legislation, he offered some key goals: protect U.S. elections from AI-generated misinformation or interference, shield U.S. workers and intellectual property, prevent exploitation by AI algorithms and create new guardrails to ward off bad actors. AI legislation also should promote American innovation, Schumer said in a speech at the Center for Strategic and International Studies, a Washington think tank. "If applied correctly, AI promises to transform life on Earth for the better," Schumer said.

The booming popularity of AI-driven chatbots like OpenAI's ChatGPT and Google's Bard has both captivated and concerned officials, who have said they are worried about again failing to protect consumers from the perils of Silicon Valley's latest craze. It's prompted lawmakers to hold a wave of public hearings and private meetings with industry leaders, researchers and advocates as they look to get their bearings in the quickly changing AI field.

People in Texas sounded off on AI job displacement, with half of people who spoke to Fox News convinced that the tech will rob them of work. The growing use of artificial intelligence will likely lead to biased and discriminatory outcomes for minorities and disabled people, several groups warned the federal government this week. The National Artificial intelligence Advisory Committee, an interagency group led by the Commerce Department, held a public hearing online Tuesday aimed at informing policymakers about how the government can best manage the use of AI. Panelists were told by most of the witnesses that bias and discrimination are the biggest fears for the people they represent. Patrice Willoughby, vice president of policy and legislative affairs at the NAACP, told panelists that technology has already been used as a means to disenfranchise and mislead voters, and said her group worries about AI for the same reason.

There is increasing attention being given to how to regulate AI systems. As governing bodies grapple with what values to encapsulate into regulation, we consider the technical half of the question: To what extent can AI experts vet an AI system for adherence to regulatory requirements? We investigate this question through two public sector procurement checklists, identifying what we can do now, what we should be able to do with technical innovation in AI, and what requirements necessitate a more interdisciplinary approach.

The RVL-CDIP benchmark is widely used for measuring performance on the task of document classification. Despite its widespread use, we reveal several undesirable characteristics of the RVL-CDIP benchmark. These include (1) substantial amounts of label noise, which we estimate to be 8.1% (ranging between 1.6% to 16.9% per document category); (2) presence of many ambiguous or multi-label documents; (3) a large overlap between test and train splits, which can inflate model performance metrics; and (4) presence of sensitive personally-identifiable information like US Social Security numbers (SSNs). We argue that there is a risk in using RVL-CDIP for benchmarking document classifiers, as its limited scope, presence of errors (state-of-the-art models now achieve accuracy error rates that are within our estimated label error rate), and lack of diversity make it less than ideal for benchmarking. We further advocate for the creation of a new document classification benchmark, and provide recommendations for what characteristics such a resource should include.