Legal scholars have postulated that there have been three eras of American law to-date, consisting in chronological order of the initial Age of Discovery, the Age of Faith, and then the Age of Anxiety. An open question that has received erudite attention in legal studies is what the next era, the fourth era, might consist of, and for which various proposals exist including examples such as the Age of Consent, the Age of Information, etc. There is no consensus in the literature as yet on what the fourth era is, and nor whether the fourth era has already begun or will instead emerge in the future. This paper examines the potential era-elucidating impacts amid the advent of autonomous Artificial Intelligence Legal Reasoning (AILR), entailing whether such AILR will be an element of a fourth era or a driver of a fourth, fifth, or perhaps the sixth era of American law. Also, a set of meta-characteristics about the means of identifying a legal era changeover are introduced, along with an innovative discussion of the role entailing legal formalism versus legal realism in the emergence of the American law eras.

We show how to assess a language model's knowledge of basic concepts of morality. We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. Models predict widespread moral judgments about diverse text scenarios. This requires connecting physical and social world knowledge to value judgements, a capability that may enable us to steer chatbot outputs or eventually regularize open-ended reinforcement learning agents. With the ETHICS dataset, we find that current language models have a promising but incomplete understanding of basic ethical knowledge. Our work shows that progress can be made on machine ethics today, and it provides a steppingstone toward AI that is aligned with human values.

Whether or not you're aware, your Google searches, questions posed to Siri, and Facebook timeline all rely on artificial intelligence (AI) to perform effectively. Artificial intelligence is the simulation of human intelligence processes by machines. The goal of artificial intelligence is to build models that can perform specific tasks as intelligently as humans can, if not better. Much of the AI you encounter on a daily basis uses a technique known as machine learning, which uses predictive modeling to generate accurate predictions when given random quantities of data. Because predictive models are built to find relational patterns in data, they learn to favor efficiency over fairness.

Big data is the confidential information storage of a company. Like how a flight's black box contains everything that happens in the journey, big data collects all the information about an organisation and stores it together. Big data describes the high volume of data that are both structured and unstructured which inundates a business on a day-to-day basis. The big data storage is spread across various computers as a single system can't manage such huge data. Big data is considered as a credible and useful source because it can be analysed with AI applications.

The global Legal Tech Artificial Intelligence Market is carefully researched in the report while largely concentrating on top players and their business …

To better understand classifiers such as those based on deep learning models, we propose a `CLAssifier-DECoder' architecture (\emph{ClaDec}). \emph{ClaDec} facilitates the comprehension of the output of an arbitrary layer in a neural network. It uses a decoder that transforms the non-interpretable representation of the given layer to a representation that is more similar to the domain a human is familiar with, such as the training data. For example, in an image recognition problem, one can recognize what information a layer maintains by contrasting reconstructed images of \emph{ClaDec} with those of a conventional auto-encoder(AE) serving as reference. An extended version of \emph{ClaDec} also allows to trade human interpretability and fidelity by customizing explanations to individual needs. We evaluate our approach for image classification using Convolutional NNs. The qualitative evaluation highlights that reconstructed images (of the network to be explained) tend to replace specific objects with more generic object templates and provide smoother reconstructions. We also show that reconstructed visualizations using encodings from a classifier do capture more relevant information for classification than conventional AEs. This holds despite the fact that AEs contain more information on the original input.

Ever since humans fell out of trees we've been creating tools to help us survive. One of the most important of those tools was writing. As our brains and species evolved up to today and the arrival of homo Sapiens, so too did our use of tools. Things are about to get a bit messier now though. As we became an agrarian society, we developed tools for farming.

The World Intellectual Property Organization (WIPO) today launched "WIPO: AI and IP, A Virtual Experience," an immersive online exhibition using the latest 360 degree scanning technology to foster a more-comprehensive understanding of the relationship between IP policy and AI and the questions facing policymakers. The exhibition is the first of its kind at WIPO and offers visitors an interactive opportunity to discover this radical new technology, while exploring some of the many ways AI promises to transform culture and industry. "This exhibition is part of a larger process of WIPO's engagement with AI, where we are having a conversation among many stakeholders to explore and develop the questions arising from the impact of AI on IP policy," said WIPO Director General Francis Gurry. "We hope users find the exhibition both educational and entertaining." The exhibition was unveiled during the Sept. 16-18 WIPO Conference on the Global Digital Content Market, which explored the latest worldwide developments in the creative industries sector brought about by digital technologies such as AI.

AI-based applications raise new, so far unresolved legal questions, and consumer law is no exception. The use of self-learning algorithms in Big Data analysis gives companies the opportunity to gain a detailed, individual insight into the customer's personal circumstances, behavior patterns and personality. On this basis, companies can tailor their advertising, but also their prices and contract terms, to the respective customer profile and – drawing on the findings of behavioral economics – exploit the consumer's biases and/or her willingness to pay. AI-based insights can also be used for scoring systems to decide whether a specific consumer can purchase a product or take up a service. The use of AI in consumer markets thus lead to a new form of power and information asymmetry.

Machine learning systems are increasingly being used to make impactful decisions such as loan applications and criminal justice risk assessments, and as such, ensuring fairness of these systems is critical. This is often challenging as the labels in the data are biased. This paper studies learning fair probability distributions from biased data by explicitly modeling a latent variable that represents a hidden, unbiased label. In particular, we aim to achieve demographic parity by enforcing certain independencies in the learned model. We also show that group fairness guarantees are meaningful only if the distribution used to provide those guarantees indeed captures the real-world data. In order to closely model the data distribution, we employ probabilistic circuits, an expressive and tractable probabilistic model, and propose an algorithm to learn them from incomplete data. We evaluate our approach on a synthetic dataset in which observed labels indeed come from fair labels but with added bias, and demonstrate that the fair labels are successfully retrieved. Moreover, we show on real-world datasets that our approach not only is a better model than existing methods of how the data was generated but also achieves competitive accuracy.