Increasingly complex learning methods such as boosting, bagging and deep learning have made ML models more accurate, but harder to understand and interpret. A tradeoff between between performance and intelligibility is often to be faced, especially in high-stakes applications like medicine. In the present article we propose a novel methodological approach for generating explanations of the predictions of a generic ML model, given a specific instance for which the prediction has been made, that can tackle both classification and regression tasks. Advantages of the proposed XAI approach include improved fidelity to the original model, ability to deal with non-linear decision boundaries, and native support to both classification and regression problems.

By bringing AI to the financial close process, Oracle claims that users can explore larger datasets in less time and waste fewer resources on mundane, repetitive tasks. Those time and resource savings could be used to allow employees in corporate finance teams and beyond to focus more on the strategic outputs of their jobs. Oracle hopes that bringing AI automation to up to 96% of transactions will result in much less time lost in the monthly race to move from the trial balance to the signing off of consolidated financials.

China's startup ecosystem, which produced several world-leading companies, has been in the news in the recent past about the impact of the government regulations on the big technology firms. However, despite the regulatory shakeups and the COVID-19 pandemic notwithstanding, China has emerged as a powerhouse for artificial intelligence (AI) unicorns, according to Thematic Research at GlobalData, a leading data, and analytics company. GlobalData's research shows that of the total of 45 AI unicorns globally, China has the biggest share with 19 unicorns headquartered in the country. These 19 unicorns are collectively valued at $43.5bn. Priya Toppo, Analyst of Thematic Research at GlobalData, comments: "China is a leading player in AI, with a number of established companies such as Baidu, Hikvision, iFlytek, Tencent, and Alibaba. The country also has a strong AI startup ecosystem, which is evident from the large number of AI unicorns (privately held startup valued at $1bn or more)."

Artificial intelligence is a hot topic having effect in many industries. This webinar will present an overview of legal discussions on artificial intelligence through the lens of current developments by government actors. The focus will be on global legal discussions, concerns, risks and opportunities that artificial intelligence poses on various industries including but not limited to mobilization, smart cities, surveillance, industrial data, and health-tech.

In its quest to drive the adoption of artificial intelligence (AI) across the country, multi-ethnic Singapore needs to take special care navigating its use in some areas, specifically, law enforcement and crime prevention. It should further foster its belief that trust is crucial for citizens to be comfortable with AI, along with the recognition that doing so will require nurturing public trust across different aspects within its society. It must have been at least two decades ago now when I attended a media briefing, during which an executive was demonstrating the company's latest speech recognition software. As most demos went, no matter how much you prepared for it, things would go desperately wrong. Her voice-directed commands often were wrongly executed and several spoken words in every sentence were inaccurately translated into text.

One of the main issues of every business process is to be compliant with legal rules. This work presents a methodology to check in a semi-automated way the regulatory compliance of a business process. We analyse an e-Health hospital service in particular: the Hospital at Home (HaH) service. The paper shows, at first, the analysis of the hospital business using the Business Process Management and Notation (BPMN) standard language, then, the formalization in Defeasible Deontic Logic (DDL) of some rules of the European General Data Protection Regulation (GDPR). The aim is to show how to combine a set of tasks of a business with a set of rules to be compliant with, using a tool.

Buying a home is an important milestone many Americans dream about. Kids grow up doodling images of their dream home. College students start building their credit early so they can apply for a mortgage in the future. People save money for years so they can afford a downpayment. But, imagine if after all that dreaming and hard work, your hopes of buying a home are dashed by a biased lending algorithm that uses your race, or where you grew up, to determine your future. According to a recent investigation conducted by The Markup, this nightmare is a reality for many prospective borrowers in the United States.

Phil Sokowicz is the co-founder and Managing Director of the legal tech company helpcheck, providing people with easy access to justice. We've seen artificial intelligence technology deployed across a number of industries, making it possible to complete laborious tasks with more speed, accuracy and efficiency. The legal sector is no different. As more firms lean into digital innovation and the use of AI to automate day-to-day tasks, AI is capable of improving services and increasing productivity there too. Although AI in the legal industry is still in its infancy, its applications are already streamlining low-value, repetitive tasks and assisting associates in simplifying the mundane and time-consuming responsibilities of legal practice.

"It is likely a matter of time until an AI will be able to simulate human thought, think creatively, and independently identify and solve problems…. If current laws remain unchanged…the owner of the AI-generated IP can and likely will attempt to protect AI-based inventions as trade secrets to the extent possible." The past few years saw a meteoric rise of artificial intelligence (AI) products, services, and applications. AI has evolved from merely a buzzword or a cool new idea to a substantively used tool in a variety of applications, including autonomous driving, natural language processing, drug development, finance and cybersecurity among others. Companies, universities, and inventors world-wide noted the importance of AI and began seeking to patent various aspects of AI technology.

E-Commerce marketplaces support millions of daily transactions, and some disagreements between buyers and sellers are unavoidable. Resolving disputes in an accurate, fast, and fair manner is of great importance for maintaining a trustworthy platform. Simple cases can be automated, but intricate cases are not sufficiently addressed by hard-coded rules, and therefore most disputes are currently resolved by people. In this work we take a first step towards automatically assisting human agents in dispute resolution at scale. We construct a large dataset of disputes from the eBay online marketplace, and identify several interesting behavioral and linguistic patterns. We then train classifiers to predict dispute outcomes with high accuracy. We explore the model and the dataset, reporting interesting correlations, important features, and insights.