Can a machine be an inventor? After the courts said no, a computer scientist is once more trying to have an artificial intelligence considered an inventor in the eyes of the law. In August, the U.S. Federal Circuit Court of Appeals issued a decision that AI cannot be listed as the inventor on a patent registration. The case before the court--Thaler v. Vidal--was either a gimmick that could be dismissed with a simple reading of U.S. patent law or one that strikes at the heart of a metaphysical question with crucial implications for the future of innovation. In Thaler v. Vidal, Stephen Thaler challenged the refusal of the U.S. Patent and Trademark Office to issue a patent registration for an invention Thaler claims was created by an artificial intelligence device called Device for Autonomous Bootstrapping of Unified Sentience, or DABUS.

Advancements in machine learning have fuelled the popularity of using AI decision algorithms in procedures such as bail hearings (Feller et al. 2016), medical diagnoses (Rajkomar et al. 2018; Esteva et al. 2019) and recruitment (Heilweil 2019, Van Esch et al. 2019). Academic articles (Floridi et al. 2018), policy texts (HLEG 2019), and popularizing books (O'Neill 2016, Eubanks 2018) alike warn that such algorithms tend to be _opaque_: they do not provide explanations for their outcomes. Building on a causal account of transparency and opacity as well as recent work on the value of causal explanation (Lombrozo 2011, Hitchcock 2012), I formulate a moral concern for opaque algorithms that is yet to receive a systematic treatment in the literature: when such algorithms are used in life-changing decisions, they can obstruct us from effectively shaping our lives according to our goals and preferences, thus undermining our autonomy. I argue that this concern deserves closer attention as it furnishes the call for transparency in algorithmic decision-making with both new tools and new challenges.

Cross-lingual transfer learning has proven useful in a variety of Natural Language Processing (NLP) tasks, but it is understudied in the context of legal NLP, and not at all in Legal Judgment Prediction (LJP). We explore transfer learning techniques on LJP using the trilingual Swiss-Judgment-Prediction dataset, including cases written in three languages. We find that cross-lingual transfer improves the overall results across languages, especially when we use adapter-based fine-tuning. Finally, we further improve the model's performance by augmenting the training dataset with machine-translated versions of the original documents, using a 3x larger training corpus. Further on, we perform an analysis exploring the effect of cross-domain and cross-regional transfer, i.e., train a model across domains (legal areas), or regions. We find that in both settings (legal areas, origin regions), models trained across all groups perform overall better, while they also have improved results in the worst-case scenarios. Finally, we report improved results when we ambitiously apply cross-jurisdiction transfer, where we further augment our dataset with Indian legal cases.

We study the problem of deleting user data from machine learning models trained using empirical risk minimization. Our focus is on learning algorithms which return the empirical risk minimizer and approximate unlearning algorithms that comply with deletion requests that come streaming minibatches. Leveraging the infintesimal jacknife, we develop an online unlearning algorithm that is both computationally and memory efficient. Unlike prior memory efficient unlearning algorithms, we target models that minimize objectives with non-smooth regularizers, such as the commonly used $\ell_1$, elastic net, or nuclear norm penalties. We also provide generalization, deletion capacity, and unlearning guarantees that are consistent with state of the art methods. Across a variety of benchmark datasets, our algorithm empirically improves upon the runtime of prior methods while maintaining the same memory requirements and test accuracy. Finally, we open a new direction of inquiry by proving that all approximate unlearning algorithms introduced so far fail to unlearn in problem settings where common hyperparameter tuning methods, such as cross-validation, have been used to select models.

Social media's growing popularity raises concerns around children's online safety. Interactions between minors and adults with predatory intentions is a particularly grave concern. Research into online sexual grooming has often relied on domain experts to manually annotate conversations, limiting both scale and scope. In this work, we test how well-automated methods can detect conversational behaviors and replace an expert human annotator. Informed by psychological theories of online grooming, we label $6772$ chat messages sent by child-sex offenders with one of eleven predatory behaviors. We train bag-of-words and natural language inference models to classify each behavior, and show that the best performing models classify behaviors in a manner that is consistent, but not on-par, with human annotation.

This story is part of Future Tense Fiction, a monthly series of short stories from Future Tense and Arizona State University's Center for Science and the Imagination about how technology and science will change our lives. By the time Tara returned from the protest, SafeT gauged her Wellness at 60% and Chase felt sick. For the last two hours he'd watched the number on his phone's app tick down, from safe green to warning yellow: 87%, 74%, 60%. On his newsfeed, masked chanters waved signs before the wire cage shielding the five megapipes that breached the marshy shore of Lake Michigan. Each pipe was owned by a consortium of Lakes United companies. Their great steel veins wormed the city, bearing water from LU to the drought-scarred West and South, whose nations paid more per acre-foot than Milwaukee's citizens ever could. On the feed Chase hadn't been able to see Tara or the sign she'd painted that morning: Our Lake, Our Water. What he had seen were the security corps of at least three consortia, clumped beneath their ever-circling camera-drones, bull-horning the chanters that they were risking corporate slander. If arrested, they'd be hauled off to one of the consortia's private prisons. There they could be coerced into confessing they were linebreakers, guerillas who spliced pipes to siphon off clean water to Milwaukee neighborhoods that couldn't afford consortia prices. Protestors sometimes returned from these prisons. Fingers numb, Chase had tapped SafeT to view the breakdown of Tara's Wellness aggregate into its individual components: risk of arrest (15%), risk of indictment (20%), risk of job loss (27%), risk of injury (31%). Even when she had texted home in 30 and he'd cleared her route in the SafeT map--low smoke risk, low contagion risk, 93% chance of safe arrival--his jaw only eased when she stepped through the door. Tara's thin face was ferocious, cheeks red against her yellow hair. Black grease spotted her strong hands. Over the decade they'd shared, he'd watched age sharpen her into herself. Now, impassioned, she was fiercely beautiful. He almost forgot her yellow number, until she saw him, and her smile sagged.

DeepMind has trained a chatbot named Sparrow to be less toxic and more accurate than other systems, by using a mix of human feedback and Google search suggestions. Chatbots are typically powered by large language models (LLMs) trained on text scraped from the internet. These models are capable of generating paragraphs of prose that are, at a surface level at least, coherent and grammatically correct, and can respond to questions or written prompts from users. This software, however, often picks up bad traits from the source material resulting in it regurgitating offensive, racist, and sexist views, or spewing fake news or conspiracies that are often found on social media and internet forums. That said, these bots can be guided to generate safer output.

Since Facebook was renamed Meta, a lot of attention, debate, and exploration have intensified about what the Metaverse is, how it works, and the possible ways to exploit it. It is anticipated that Metaverse will be a continuum of rapidly emerging technologies, usecases, capabilities, and experiences that will make it up for the next evolution of the Internet. Several researchers have already surveyed the literature on artificial intelligence (AI) and wireless communications in realizing the Metaverse. However, due to the rapid emergence and continuous evolution of technologies, there is a need for a comprehensive and in-depth survey of the role of AI, 6G, and the nexus of both in realizing the immersive experiences of Metaverse. Therefore, in this survey, we first introduce the background and ongoing progress in augmented reality (AR), virtual reality (VR), mixed reality (MR) and spatial computing, followed by the technical aspects of AI and 6G. Then, we survey the role of AI in the Metaverse by reviewing the state-of-the-art in deep learning, computer vision, and Edge AI to extract the requirements of 6G in Metaverse. Next, we investigate the promising services of B5G/6G towards Metaverse, followed by identifying the role of AI in 6G networks and 6G networks for AI in support of Metaverse applications, and the need for sustainability in Metaverse. Finally, we enlist the existing and potential applications, usecases, and projects to highlight the importance of progress in the Metaverse. Moreover, in order to provide potential research directions to researchers, we underline the challenges, research gaps, and lessons learned identified from the literature review of the aforementioned technologies.

Distributed machine learning enables scalability and computational offloading, but requires significant levels of communication. Consequently, communication efficiency in distributed learning settings is an important consideration, especially when the communications are wireless and battery-driven devices are employed. In this paper we develop a censoring-based heavy ball (CHB) method for distributed learning in a server-worker architecture. Each worker self-censors unless its local gradient is sufficiently different from the previously transmitted one. The significant practical advantages of the HB method for learning problems are well known, but the question of reducing communications has not been addressed. CHB takes advantage of the HB smoothing to eliminate reporting small changes, and provably achieves a linear convergence rate equivalent to that of the classical HB method for smooth and strongly convex objective functions. The convergence guarantee of CHB is theoretically justified for both convex and nonconvex cases. In addition we prove that, under some conditions, at least half of all communications can be eliminated without any impact on convergence rate. Extensive numerical results validate the communication efficiency of CHB on both synthetic and real datasets, for convex, nonconvex, and nondifferentiable cases. Given a target accuracy, CHB can significantly reduce the number of communications compared to existing algorithms, achieving the same accuracy without slowing down the optimization process.

Different linguistic expressions can conceptualize the same event from different viewpoints by emphasizing certain participants over others. Here, we investigate a case where this has social consequences: how do linguistic expressions of gender-based violence (GBV) influence who we perceive as responsible? We build on previous psycholinguistic research in this area and conduct a large-scale perception survey of GBV descriptions automatically extracted from a corpus of Italian newspapers. We then train regression models that predict the salience of GBV participants with respect to different dimensions of perceived responsibility. Our best model (fine-tuned BERT) shows solid overall performance, with large differences between dimensions and participants: salient _focus_ is more predictable than salient _blame_, and perpetrators' salience is more predictable than victims' salience. Experiments with ridge regression models using different representations show that features based on linguistic theory similarly to word-based features. Overall, we show that different linguistic choices do trigger different perceptions of responsibility, and that such perceptions can be modelled automatically. This work can be a core instrument to raise awareness of the consequences of different perspectivizations in the general public and in news producers alike.