One of the most recurrent discussions in legal-philosophical debates has been the legal science theory, which in fact have not ever come to a single final conclusion considering that there is not ever a single correct theory in law. A new dimension may now be added to these debates, given that the discipline of law has, through legal technology applications, approached to natural sciences as it has never ever. Accordingly, the alternative future designed for legal education and practice of law, as we have tried to describe throughout our paper, contemplates that the adoption of an experiential education in legal education may pave the way for change of role of attorney in legal professions. Another issue deemed worthy of debate in this context is whether the legal language may be expressed in the form of an algorithm or not. According to the opinion defended in this paper, although the language of law, differently from computer language, is far beyond the expressibility as ones and zeroes, i.e. rights and wrongs, it is also important to overview this language from a different point of view, getting out of the traditional patterns.

With in-house teams around the world grappling with growing numbers of contracts stored across multiple repositories and increased pressure to comply with complex regulation, Luminance's new Corporate product will allow in-house counsel to save time and resource, streamline legal processes and gain greater insight into their contractual landscape. Instead of manually sifting through contracts in order to identify and collate key information, Luminance's AI-powered technology will give in-house teams immediate insight into their contracts, highlighting key features such as clauses, parties and Governing Law. Crucially, for the first time ever, in-house lawyers will be able to tap into Luminance's powerful machine learning for contract analysis from within Microsoft Word, all during the regular workflow of their review. For contracts under negotiation, Luminance has the ability to proactively search documents to see what changes have been made by counterparties, including those changes which may have been conducted without track changes being switched on in Word. When negotiating on a specific clause, Luminance will show lawyers precedent at the click of a button, highlighting areas where conceptually similar clauses have been previously signed off by their organisation.

We present the Multiple Criteria Decision Analysis Methods Selection Software (MCDA-MSS). This decision support system helps analysts answering a recurring question in decision science: Which is the most suitable Multiple Criteria Decision Analysis method (or a subset of MCDA methods) that should be used for a given Decision-Making Problem (DMP)?. The MCDA-MSS includes guidance to lead decision-making processes and choose among an extensive collection (over 200) of MCDA methods. These are assessed according to an original comprehensive set of problem characteristics. The accounted features concern problem formulation, preference elicitation and types of preference information, desired features of a preference model, and construction of the decision recommendation. The applicability of the MCDA-MSS has been tested on several case studies. The MCDA-MSS includes the capabilities of (i) covering from very simple to very complex DMPs, (ii) offering recommendations for DMPs that do not match any method from the collection, (iii) helping analysts prioritize efforts for reducing gaps in the description of the DMPs, and (iv) unveiling methodological mistakes that occur in the selection of the methods. A community-wide initiative involving experts in MCDA methodology, analysts using these methods, and decision-makers receiving decision recommendations will contribute to expansion of the MCDA-MSS.

We study the question of how well machine learning (ML) models trained on a certain data set provide privacy for the training data, or equivalently, whether it is possible to reverse-engineer the training data from a given ML model. While this is easy to answer negatively in the most general case, it is interesting to note that the protection extends over non-recoverability towards plausible deniability: Given an ML model $f$, we show that one can take a set of purely random training data, and from this define a suitable ``learning rule'' that will produce a ML model that is exactly $f$. Thus, any speculation about which data has been used to train $f$ is deniable upon the claim that any other data could have led to the same results. We corroborate our theoretical finding with practical examples, and open source implementations of how to find the learning rules for a chosen set of raining data.

Intent modifies an actor's culpability of many types wrongdoing. Autonomous Algorithmic Agents have the capability of causing harm, and whilst their current lack of legal personhood precludes them from committing crimes, it is useful for a number of parties to understand under what type of intentional mode an algorithm might transgress. From the perspective of the creator or owner they would like ensure that their algorithms never intend to cause harm by doing things that would otherwise be labelled criminal if committed by a legal person. Prosecutors might have an interest in understanding whether the actions of an algorithm were internally intended according to a transparent definition of the concept. The presence or absence of intention in the algorithmic agent might inform the court as to the complicity of its owner. This article introduces definitions for direct, oblique (or indirect) and ulterior intent which can be used to test for intent in an algorithmic actor.

Artificial intelligence could lead to an Orwellian future if laws to protect the public aren't enacted soon, according to Microsoft President Brad Smith. Smith made the comments to the BBC news program "Panorama" on May 26, during an episode focused on the potential dangers of artificial intelligence (AI) and the race between the United States and China to develop the technology. The warning comes about a month after the European Union released draft regulations attempting to set limits on how AI can be used. There are few similar efforts in the United States, where legislation has largely focused on limiting regulation and promoting AI for national security purposes. "I'm constantly reminded of George Orwell's lessons in his book '1984,'" Smith said.

Since graduating from a US university four years ago, Kevin Carballo has lost count of the number of times he has applied for a job only to receive a swift, automated rejection email - sometimes just hours after applying. Like many job seekers around the world, Mr Carballo's applications are increasingly being screened by algorithms built to automatically flag attractive applicants to hiring managers. "There's no way to apply for a job these days without being analysed by some sort of automated system," said Mr Carballo, 27, who is latino and the first member of his family to go to university. "It feels like shooting in the dark while being blindfolded - there's just no way for me to tell my full story when a machine is assessing me," Mr Carballo, who hoped to get work experience at a law firm before applying to law school, told the Thomson Reuters Foundation by phone. From Artificial Intelligence (AI) programs that assess an applicant's facial expressions during a video interview, to resume screening platforms predicting job performance, the AI recruitment industry is valued at more than $500 million (£350 million).

LOS ANGELES – Since graduating from a U.S. university four years ago, Kevin Carballo has lost count of the number of times he has applied for a job only to receive a swift, automated rejection email -- sometimes just hours after applying. Like many job seekers around the world, Carballo's applications are increasingly being screened by algorithms built to automatically flag attractive applicants to hiring managers. "There's no way to apply for a job these days without being analyzed by some sort of automated system," said Carballo, 27, who is Latino and the first member of his family to go to university. "It feels like shooting in the dark while being blindfolded -- there's just no way for me to tell my full story when a machine is assessing me," Carballo, who hoped to get work experience at a law firm before applying to law school, said by phone. From artificial intelligence programs that assess an applicant's facial expressions during a video interview, to resume screening platforms predicting job performance, the AI recruitment industry is valued at more than $500 million.

Thomson Reuters is a leading multinational conglomerate that provides trusted data and information across different industries. The company mainly serves Legal, Tax and Accounting, and Media. Artificial intelligence is the norm that industries are rapidly adopting today. AI and other disruptive technologies like machine learning are enabling businesses to gain drive efficiency and growth. Companies are highly investing in AI and launching innovations.

Originality criteria are frequently used to compare assets and, in particular, to assess the validity of intellectual property (IP) rights such as copyright and design rights. In this work, the originality of an asset is formulated as a function of the distances between this asset and its comparands, using concepts of maximum entropy and surprisal analysis. Namely, the originality function is defined according to the surprisal associated with a given asset. Creative assets can be justifiably compared to particles that repel each other via an electrostatic-like pair potential. This allows a very simple, suitably bounded formula to be obtained, in which the originality of an asset writes as the ratio of a reference energy to an interaction energy imparted to that asset. In particular, the originality of an asset can be expressed as a ratio of two average distances, i.e., the harmonic mean of the distances from this asset to its comparands divided by the harmonic mean of the distances between the sole comparands. Accordingly, the originality of objects such as IP assets can be simply estimated based on distances computed thanks to unsupervised machine learning techniques or other distance computation algorithms. Application is made to various types of assets, including emojis, typeface designs, paintings, and novel titles.