Jean-Luc Godard once claimed, regarding cinema, "When I die, it will be the end." Godard passed away last month; film perseveres. Yet artificial intelligence has raised a kindred specter: that humans may go obsolete long before their artistic mediums do. Novels scribed by GPT-3; art conjured by DALL·E--machines could be making art long after people are gone. As deepfakes evolve, fears are mounting that future films, TV shows, and commercials may not need them at all.

The European Commission has published (28 September 2022) proposals for adapting civil litigation rules in European Union Member States – and in the European Economic Area – to reduce perceived difficulties in claiming non-contractual damages for harm caused by artificial intelligence (AI). The proposal sits alongside wider reforms to the product liability regime. Both are closely intertwined with the EU's proposed AI Act. The AI liability reforms are aimed at making it less burdensome for claimants to secure compensation, with the intention of promoting trust in this increasingly pervasive technology. Claimants in civil law systems (typically without common law-style disclosure obligations) often have much less information than the defendant about the events that they believe have caused harm to them.

Greg Rutkowski is an artist with a distinctive style: He's known for creating fantasy scenes of dragons and epic battles that fantasy games like Dungeons and Dragons have used. He said it used to be "really rare to see a similar style to mine on the internet." Yet if you search for his name on Twitter, you'll see plenty of images in his exact style -- that he didn't make. Rutkowski has become one of the most popular names in AI art, despite never having used the technology himself. People are creating thousands of artworks that look like his using programs called AI-image generators, which use artificial intelligence to create original artwork in minutes or even seconds after a user types in a few words as directions.

Adrienne Williams and Milagros Miceli are researchers at the Distributed AI Research (DAIR) Institute. Timnit Gebru is the institute's founder and executive director. She was previously co-lead of the Ethical AI research team at Google. The public's understanding of artificial intelligence (AI) is largely shaped by pop culture -- by blockbuster movies like "The Terminator" and their doomsday scenarios of machines going rogue and destroying humanity. This kind of AI narrative is also what grabs the attention of news outlets: a Google engineer claiming that its chatbot was sentient was among the most discussed AI-related news in recent months, even reaching Stephen Colbert's millions of viewers.

Today, we see how computer programs, algorithms, and robots replace simple human activities, but there is the technology that is at the forefront of the spectrum: AI. The consequences of artificial intelligence have such an impact that they incite us to wonder if we are experiencing the beginning of a new era. According to Gartner, business use of AI has grown by 270% in the last four years, and slightly more than a third of organizations have implemented AI in some way, according to their specific needs. But is this also a reality within the legal sector? AI has found its way to support attorneys and clients alike, and there is a clear growing interest in technology.

As ML models have increased in capabilities and accuracy, so has the complexity of their deployments. Increasingly, ML model consumers are turning to service providers to serve the ML models in the ML-as-a-service (MLaaS) paradigm. As MLaaS proliferates, a critical requirement emerges: how can model consumers verify that the correct predictions were served, in the face of malicious, lazy, or buggy service providers? In this work, we present the first practical ImageNet-scale method to verify ML model inference non-interactively, i.e., after the inference has been done. To do so, we leverage recent developments in ZK-SNARKs (zero-knowledge succinct non-interactive argument of knowledge), a form of zero-knowledge proofs. ZK-SNARKs allows us to verify ML model execution non-interactively and with only standard cryptographic hardness assumptions. In particular, we provide the first ZK-SNARK proof of valid inference for a full resolution ImageNet model, achieving 79\% top-5 accuracy. We further use these ZK-SNARKs to design protocols to verify ML model execution in a variety of scenarios, including for verifying MLaaS predictions, verifying MLaaS model accuracy, and using ML models for trustless retrieval. Together, our results show that ZK-SNARKs have the promise to make verified ML model inference practical.

We study automatic Contract Clause Extraction (CCE) by modeling implicit relations in legal contracts. Existing CCE methods mostly treat contracts as plain text, creating a substantial barrier to understanding contracts of high complexity. In this work, we first comprehensively analyze the complexity issues of contracts and distill out three implicit relations commonly found in contracts, namely, 1) Long-range Context Relation that captures the correlations of distant clauses; 2) Term-Definition Relation that captures the relation between important terms with their corresponding definitions; and 3) Similar Clause Relation that captures the similarities between clauses of the same type. Then we propose a novel framework ConReader to exploit the above three relations for better contract understanding and improving CCE. Experimental results show that ConReader makes the prediction more interpretable and achieves new state-of-the-art on two CCE tasks in both conventional and zero-shot settings.

Financial regulation is forever running to catch up with evolving technology. There are many examples of this: the Second Markets in Financial Instruments Directive (MiFID II) sought to make up ground on the increased electronification of markets since the introduction of MiFID I; policymakers in both the EU and the UK are at this very moment defining the regulatory perimeter around cryptoassets, more than a decade after the initial launch of bitcoin; and regulators first took action against runaway algorithms long before restrictions on algorithmic trading made it into regulatory rulebooks. Continuing this trend, on 11 October 2022, the Bank of England (BoE) and the UK Financial Conduct Authority (FCA) launched a joint discussion paper on how the UK regulators should approach the "safe and responsible" adoption of AI in financial services (FCA DP22/4 and BoE DP5/22) (the AI Discussion Paper), which is now open for responses. This follows the UK Government's Command Paper published in July 2022, announcing a "pro-innovation" approach to regulating AI (CP 728) across different sectors. One strong theme that comes out of the AI Discussion Paper is that, notwithstanding the potential benefits of AI in fostering innovation and reducing costs in financial services, the human factor is key to ensure that AI is governed and overseen responsibly and that potential negative impacts on clients and other stakeholders are mitigated appropriately.

Google is a strange company. Sometimes it seems like a testing center for machine learning algorithms. It is also in a strange place when it comes to the smartphone industry. While Google knows its future is almost entirely dependent on the smartphone, it has to mix being the caretaker of Android with selling phones itself and building services that work across platforms. This is why Google gets more regulatory attention than Apple -- an even bigger, wealthier, and more heavy-handed company.

Across the AI ethics and global policy landscape, there is consensus that there should be human accountability for AI technologies1. These machines are used for high-stakes decision-making in complex domains -- for example, in healthcare, criminal justice and transport -- where they can cause or occasion serious harm. Some use deep machine learning models, which can make their outputs difficult to understand or contest. At the same time, when the datasets on which these models are trained reflect bias against specific demographic groups, the bias becomes encoded and causes disparate impacts2,3,4. Meanwhile, an increasing number of machines that embody AI, and specifically machine learning, such as highly automated vehicles, can execute decision-making functions and take actions independently of direct, real-time human control, in unpredictable conditions that call for adaptive performance.