Art is subjective and everyone has their own opinion about it. When I saw the expressionist painting Blue Poles, by Jackson Pollock, I was reminded of the famous quote by Rudyard Kipling, "It's clever, but is it Art?" Pollock's piece looks like paint messily spilled onto a drop sheet protecting the floor. The debate of what constitutes art has a long history that will probably never be settled, there is no definitive definition of art. Similarly, there is no broadly accepted objective definition for the quality of a piece of art, with the closest definition being from Orson Welles, "I don't know anything about art but I know what I like." Similarly, people recognize unfair bias when they see it, but it is quite difficult to create a single objective definition.

It is not just organizations based in the EU that need to pay attention. The regulation will apply to any provider that implements or develops AI systems in the EU or whose AI systems produce outputs that are used in the EU's jurisdiction, so it will affect many organizations based elsewhere. Moreover, the regulation, which is expected to come into force in 2023, is likely to bear similarities to rules currently being drawn up by other government authorities throughout the world.2 Given the impending heightened focus on new regulations, as well as the potential financial and reputational damage resulting from noncompliance, organizations urgently need to adopt measures that enable them to comply with the requirements of the emerging EU regulation. A comprehensive RAI program, based on BCG's Responsible AI Leader Blueprint, will allow them to act in accordance with and adapt to the proposed EU AI Act and other regulations that will inevitably follow (such as the Algorithmic Accountability Act of 2022 in the US).3 Notes: 3 US Congress, 2022, "Algorithmic Accountability Act of 2022."

Artists have always learned from other artists. Ogbogu Kalu, for instance, a Nigerian engineer in Canada, has created AI models trained to emulate the comic-book style of Holly Mengert and James Daly III -- without the permission of either illustrator. As Mengert points out to Andy Baio, she couldn't give Kalu permission to train his model on her work even if she wanted to, because so much of what she does involves characters owned by corporations like Disney or Penguin Random House. Artists have always learned from other artists. Ogbogu Kalu, for instance, a Nigerian engineer in Canada, has created AI models trained to emulate the comic-book style of Holly Mengert and James Daly III -- without the permission of either illustrator.

Though many algorithms can be used to automatically summarize legal case decisions, most fail to incorporate domain knowledge about how important sentences in a legal decision relate to a representation of its document structure. For example, analysis of a legal case summarization dataset demonstrates that sentences serving different types of argumentative roles in the decision appear in different sections of the document. In this work, we propose an unsupervised graph-based ranking model that uses a reweighting algorithm to exploit properties of the document structure of legal case decisions. We also explore the impact of using different methods to compute the document structure. Results on the Canadian Legal Case Law dataset show that our proposed method outperforms several strong baselines.

To receive The Algorithm newsletter in your inbox every Monday, sign up here. It's never been more important for companies to ensure that their AI systems function safely, especially as new laws to hold them accountable kick in. The responsible AI teams they set up to do that are supposed to be a priority, but investment in it is still lagging behind. People working in the field suffer as a result, as I found in my latestpiece. Organizations place huge pressure on individuals to fix big, systemic problems without proper support, while they often face a near-constant barrage of aggressive criticism online.

A recently presented European Union plan to update long-standing product liability rules for the digital age -- including addressing rising use of artificial intelligence (AI) and automation -- took some instant flak from European consumer organization, BEUC, which framed the update as something of a downgrade by arguing EU consumers will be left less well protected from harms caused by AI services than other types of products. For a flavor of the sorts of AI-driven harms and risks that may be fuelling demands for robust liability protections, only last month the UK's data protection watchdog issued a blanket warning over pseudoscientific AI systems that claim to perform'emotional analysis' -- urging such tech should not be used for anything other than pure entertainment. While on the public sector side, back in 2020, a Dutch court found an algorithmic welfare risk assessment for social security claimants breached human rights law. And, in recent years, the UN has also warned over the human rights risks of automating public service delivery. Additionally, US courts' use of blackbox AI systems to make sentencing decisions -- opaquely baking in bias and discrimination -- has been a tech-enabled crime against humanity for years. BEUC, an umbrella consumer group which represents 46 independent consumer organisations from 32 countries, had been calling for years for an update to EU liability laws to take account of growing applications of AI and ensure consumer protections laws are not being outpaced.

When it comes to languages, many problems arise in typical translation services. Either it is bad grammar or the translation does not completely make sense afterward. It is essential that these mistakes do not fall through during the final translation, whether it's during a business transaction or simply a conversation. Luckily, technology has advanced this process with the help of automation and artificial intelligence, assisting with speed and accuracy. In this article, we will discuss some of the most prominent and up-and-coming companies that provide these automated solutions that break down the language barrier.

Pre-training large transformer models with in-domain data improves domain adaptation and helps gain performance on the domain-specific downstream tasks. However, sharing models pre-trained on potentially sensitive data is prone to adversarial privacy attacks. In this paper, we asked to which extent we can guarantee privacy of pre-training data and, at the same time, achieve better downstream performance on legal tasks without the need of additional labeled data. We extensively experiment with scalable self-supervised learning of transformer models under the formal paradigm of differential privacy and show that under specific training configurations we can improve downstream performance without sacrifying privacy protection for the in-domain data. Our main contribution is utilizing differential privacy for large-scale pre-training of transformer language models in the legal NLP domain, which, to the best of our knowledge, has not been addressed before.

We present a new NLP task and dataset from the domain of the U.S. civil procedure. Each instance of the dataset consists of a general introduction to the case, a particular question, and a possible solution argument, accompanied by a detailed analysis of why the argument applies in that case. Since the dataset is based on a book aimed at law students, we believe that it represents a truly complex task for benchmarking modern legal language models. Our baseline evaluation shows that fine-tuning a legal transformer provides some advantage over random baseline models, but our analysis reveals that the actual ability to infer legal arguments remains a challenging open research question.

Innovative companies in virtually every industry--from healthcare and investing to transportation and manufacturing--are rapidly adopting artificial intelligence (AI) and machine learning to accomplish sophisticated tasks with increasing accuracy and precision. As these revolutionary technologies evolve and improve, they are becoming an important element in enhancing an ever-growing list of applications, including customer service, logistics, and safety. Wilson Sonsini's artificial intelligence and machine learning team has worked with hundreds of companies in the AI space. Clients rely on us to help them protect and commercialize their AI technologies, in-license AI technologies from start-ups and academic institutions, litigate AI-related IP disputes, and navigate the complex legal and regulatory landscape governing this dynamic field. Because many of our team's attorneys and staff professionals have relevant technical backgrounds, we understand the unique nuances and challenges of this field.