Companies, governments, and other institutions started embedding artificial intelligence into their products, services, processes, and decision-making to a great extent. This opened great questions on how the data is used by their systems and if any, what are the implications. The answers become even more serious if we take the complex, evolving algorithms that propose health diagnosis, approve a loan, or even autonomously drive a car. Now more than ever, it is essential to develop AI tools that can be trusted and are responsible as AI has and will have wide-ranging economic impacts across manufacturing, transportation, health, education, and many other sectors. This can be done by the development of public sector policies and laws for promoting and regulating AI. It is quite a recent topic among regulators globally as between 2016 and 2020 a wave of AI regulations and guidelines were published in order to maintain social control over the use of algorithms in our everyday lives.

Reviewing contracts is a time-consuming procedure that incurs large expenses to companies and social inequality to those who cannot afford it. In this work, we propose "document-level natural language inference (NLI) for contracts", a novel, real-world application of NLI that addresses such problems. In this task, a system is given a set of hypotheses (such as "Some obligations of Agreement may survive termination.") and a contract, and it is asked to classify whether each hypothesis is "entailed by", "contradicting to" or "not mentioned by" (neutral to) the contract as well as identifying "evidence" for the decision as spans in the contract. We annotated and release the largest corpus to date consisting of 607 annotated contracts. We then show that existing models fail badly on our task and introduce a strong baseline, which (1) models evidence identification as multi-label classification over spans instead of trying to predict start and end tokens, and (2) employs more sophisticated context segmentation for dealing with long documents. We also show that linguistic characteristics of contracts, such as negations by exceptions, are contributing to the difficulty of this task and that there is much room for improvement.

We've all seen movies where AI takes over the world (I, Robot is probably my favorite) but what are the potential harms of it in the current day. Let's try and understand from where can these dangers arise in the first place. Modern AI uses various black-box algorithms where they get the desired results but the reasoning for it performing better or equivalent to humans might be lost in the process or rarely ever evaluated. Now you might be wondering if we control the results, how is it going to take over the world, the answer is it probably won't. What can go wrong though, is its ability to obtain results wanted by companies or organizations by crossing moral or legal boundaries without anybody knowing or realizing not even the companies themselves.

We are used to looking only at well-defined and delimited fields, where business thrives and goes on, and where economic resources and technological availability make the road to innovation more straightforward. However, in my opinion, we never stop to analyse what Shakir Mohamed, in his "Decolonial AI", defines as the "peripheries", shifting our "ictu oculi" from the centre towards new paradigms, still unexplored, if not ignored. Therefore, I found this study by Agunbiade Akintunde Ifeanyichukwu, whose name already says it all, since he signs himself Agunbiade A.I., which analyses the relationship between Artificial Intelligence (AI) and the Nigerian legal system, entitled "Artificial Intelligence and Law, a Nigerian Perspective", really interesting. The aim was to explore the ways in which they can influence each other, capturing new and half-known aspects of little-discussed legal systems. This book proposed the development of an indigenous AI system, coupled with ADR mechanisms, that would have the power to reduce the incidence of court congestion, while analysing a comprehensive legal framework of how it would work.

We have some of the most brilliant and hardworking people in the world working with us and our engineering teams are growing fast in some of the hottest state of the art fields: Deep Learning, Artificial Intelligence, and Autonomous Vehicles. If you're a creative computer scientist/engineer with a real passion for distributed systems and autonomous driving, we want to hear from you.The Colorado Equal Pay for Equal Work Act requires that NVIDIA provide the compensation range and benefits offered for this position if performed in Colorado. The base salary range for this position in Colorado is $190,800.00

Fast developing artificial intelligence (AI) technology has enabled various applied systems deployed in the real world, impacting people's everyday lives. However, many current AI systems were found vulnerable to imperceptible attacks, biased against underrepresented groups, lacking in user privacy protection, etc., which not only degrades user experience but erodes the society's trust in all AI systems. In this review, we strive to provide AI practitioners a comprehensive guide towards building trustworthy AI systems. We first introduce the theoretical framework of important aspects of AI trustworthiness, including robustness, generalization, explainability, transparency, reproducibility, fairness, privacy preservation, alignment with human values, and accountability. We then survey leading approaches in these aspects in the industry. To unify the current fragmented approaches towards trustworthy AI, we propose a systematic approach that considers the entire lifecycle of AI systems, ranging from data acquisition to model development, to development and deployment, finally to continuous monitoring and governance. In this framework, we offer concrete action items to practitioners and societal stakeholders (e.g., researchers and regulators) to improve AI trustworthiness. Finally, we identify key opportunities and challenges in the future development of trustworthy AI systems, where we identify the need for paradigm shift towards comprehensive trustworthy AI systems.

The European Union (EU) through the High-Level Expert Group on Artificial Intelligence (AI-HLEG) and the General Data Protection Regulation (GDPR) has recently posed an interesting challenge to the eXplainable AI (XAI) community, by demanding a more user-centred approach to explain Automated Decision-Making systems (ADMs). Looking at the relevant literature, XAI is currently focused on producing explainable software and explanations that generally follow an approach we could term One-Size-Fits-All, that is unable to meet a requirement of centring on user needs. One of the causes of this limit is the belief that making things explainable alone is enough to have pragmatic explanations. Thus, insisting on a clear separation between explainabilty (something that can be explained) and explanations, we point to explanatorY AI (YAI) as an alternative and more powerful approach to win the AI-HLEG challenge. YAI builds over XAI with the goal to collect and organize explainable information, articulating it into something we called user-centred explanatory discourses. Through the use of explanatory discourses/narratives we represent the problem of generating explanations for Automated Decision-Making systems (ADMs) into the identification of an appropriate path over an explanatory space, allowing explainees to interactively explore it and produce the explanation best suited to their needs.

Social scientists amply document algorithmic harms and algorithmic bias, such as discrimination in hiring, medical settings, or the criminal justice system, and for good reason -- these systems produce wide effects and are deployed on a massive scale. Yet there is less attention on how different forms of pleasure, affect, and desire produce and drive both normative and renegade repurposings of these systems. Pleasure, or pleasures, which I take to encompass the expressive life, range of feelings, affective charges routed through technical systems, and the systems of drives that animate social, ecological, and technical worlds, needs to be thought of as an essential, and not always positive, aspect of our technological systems. As AI systems develop and critiques of these systems mount, we will have to come to terms with the following realities: First, forms of desire for control, power, knowledge, and progress gave rise to techno-solutionism in the first place, and repudiating those forms will require cultivating other modes of pleasure. The belief that the problems produced by algorithms can be solved by ever newer forms of technology is deep-seeded and seated in a heady mixture of (white) male cultural norms, ideas about progress that treat those on the receiving end of technological harms as'backward,' and colonial norms that separate out a particular technology from the larger environmental, economic, social, and cultural contexts in which they unfold (Ricaurte 2019, Ullman 2017, Broussard 2018, Forsythe 2001, Heyward-Rotimi 2021).

Join our XPotential Community, future proof yourself with courses from XPotential University, connect, watch a keynote, or browse my blog. Artificial Intelligence (AI) has been getting creative for some time now and inventing new things, including everything from new kinds of batteries, computer chips, furniture, and rocket engines, all the way through to new kinds of vehicles and sports apparel, for companies as diverse as Airbus, Amazon, GM, NASA, and Under Armour. But despite this quantum leap recently the US Patent Office declined to credit AI for its inventions. Now that's changed, and in what seems to be a world first Intellectual property (IP) officials in South Africa have made history in a landmark decision to award a patent that names an AI as the inventor. The patent – which was filed by an international team of lawyers and researchers led by the University of Surrey's, Professor of Law and Health Sciences, Ryan Abbott – is for a food container based on fractal geometry.

This week includes free AI training to close the digital divide, EU's tech startup winning formula, can existing technologies solve the climate challenge and more.