Artificial intelligence is being deployed in many different areas. Within higher education, it is used for college admissions and financial aid decisions. Health researchers employ it to scan the scientific literature for chemical compounds that may generate new medical treatments. E-commerce sites deploy algorithms to make product recommendations for consumers based on their areas of interest.1 But one of the most important growth areas lies in finance and operations. Both public and private sector organizations have large budgets to manage and it is important to operate efficiently and effectively. Accusations of budget inefficiencies or wasteful spending decrease public confidence and make it important to figure out how to manage resources in fair ways. To help with budgetary oversight, AI is being used for financial management and fraud detection. Advanced algorithms can spot abnormalities and outliers that can be referred to human investigators to determine if fraud actually has taken place. It is a way to use technology to improve budget audits, personnel performance, and organizational activities. Yet is it crucial to overcome several problems that plague public sector innovation: procurement obstacles, insufficiently trained workers, data limitations, a lack of technical standards, cultural barriers to organizational change, and making sure anti-fraud applications adhere to responsible AI principles.

Many industries are utilizing AI. However, in this paper, we look at its applications in the aerospace, fintech, autonomous vehicles, and health care industries, where better AI hardware, software, solutions, and services are creating many opportunities. Data integrity, privacy policies, decision system guidelines, and holistic regulations are continuously evolving in these industries. This ecosystem is now ripe for service providers and system integrators to play their parts, with AI adoption achieving appreciable return on investment. Key applications of AI in this space include optimizing operational efficiencies, assuring robustness of systems, data and image interpretation, and human augmented decision-making. Other applications include automation of processes and workflows, better compliance, improved performance, and reliability platforms, unmanned derivative systems (in finance) and digital and virtual assistants. Figure 1 summarizes AI's importance across the four industries discussed in this paper.1-36 The primary drivers of AI are data privacy, security, cost, risk, authenticity, guarantee and improved decision systems. Each driver has its own specific impact and relevance from a business adoption and operations perspective. The driver ensures that applications will have business significance and are attuned to regulations, while having close association with global and geography-specific ecosystems. Also, the drivers ensure quicker adoption to enhance operational efficiency, without compromising on the end-user experience. Regulatory and government bodies play a vital role in assessing and formulating guidelines for adopting AI in the business value chain.

The EU is winning the battle for trust among artificial intelligence (AI) researchers, academics on both sides of the Atlantic say, bolstering the Commission's ambitions to set global standards for the technology. But some fear the EU risks squandering this confidence by imposing ill-thought through rules in its recently proposed Artificial Intelligence act, which some academics say are at odds with the realities of AI research. "We do see a push for trustworthy and transparent AI also in the US, but, in terms of governance, we are not as far [ahead] as the EU in this regard," said Bart Selman, president of the Association for Advancement of Artificial Intelligence (AAAI) and a professor at Cornell University. Highly international AI researchers are "aware that AI developments in the US are dominated by business interests, and in China by the government interest," said Holger Hoos, professor of machine learning at Leiden University, and a founder of the Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE). EU policymaking, though slower, incorporated "more voices, and more perspectives" than the more centralised process in the US and China, he argued, with the EU having taken strong action on privacy through the General Data Protection regulation, which came into effect in 2018.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. In April 2020, news broke that Banjo CEO Damien Patton, once the subject of profiles by business journalists, was previously convicted of crimes committed with a white supremacist group. According to OneZero's analysis of grand jury testimony and hate crime prosecution documents, Patton pled guilty to involvement in a 1990 shooting attack on a synagogue in Tennessee. Amid growing public awareness about algorithmic bias, the state of Utah halted a $20.7 million contract with Banjo, and the Utah attorney general's office opened an investigation into matters of privacy, algorithmic bias, and discrimination. But in a surprise twist, an audit and report released last week found no bias in the algorithm because there was no algorithm to assess in the first place.

I found really interesting a study from 2020, titled "Legal liability for Autonomous Robotics", made by Dr. Safaa Fatouh Gomaa, Member of the Faculty of Law of the Egyptian Mansoura University, a study related to legal issues regarding liability related to Artificial Intelligence products, but more specifically, in the production of autonomous robotics. According to the European resolutions of 2017 and 2018, according to Gomaa, the liability rules cover cases where the cause of the robot's actions or missteps can be attributed to a specific human agent such as the manufacturer, the machinist, the holder or the manager, and where this representative could have foreseen and circumvented the robot's dangerous conduct. He also adds that since digital technologies are constantly evolving, due to, patches, updates and software extensions, influencing the behaviour of all mechanisms of the system, it is crucial to identify responsibilities among the different actors in the AI supply chain. Given the complexity of the topic to be covered, the researcher has divided the paper into three sections; section 1 is the historical, international and legal framework for Robots; section 2 is about identifying the legal responsibility for autonomous industrial robotics; finally, section 3 gives an overview of his conclusions. Robot concepts began as legends.

Numerous government initiatives (e.g. the EU with GDPR) are coming to the conclusion that the increasing complexity of modern software systems must be contrasted with some Rights to Explanation and metrics for the Impact Assessment of these tools, that allow humans to understand and oversee the output of Automated Decision Making systems. Explainable AI was born as a pathway to allow humans to explore and understand the inner working of complex systems. But establishing what is an explanation and objectively evaluating explainability, are not trivial tasks. With this paper, we present a new model-agnostic metric to measure the Degree of eXplainability of correct information in an objective way, exploiting a specific model from Ordinary Language Philosophy called the Achinstein's Theory of Explanations. In order to understand whether this metric is actually behaving as explainability is expected to, we designed a few experiments and a user-study on two realistic AI-based systems for healthcare and finance, involving famous AI technology including Artificial Neural Networks and TreeSHAP. The results we obtained are very encouraging, suggesting that our proposed metric for measuring the Degree of eXplainability is robust on several scenarios and it can be eventually exploited for a lawful Impact Assessment of an Automated Decision Making system.

Striking a blow to patent applicants seeking to assert inventorship by artificial intelligence ("AI") systems, the U.S. District Court for the Eastern District of Virginia ruled on September 3, 2021 that an AI machine cannot qualify as an "inventor" under the Patent Act. The fight is now expected to move to the Federal Circuit on appeal. Proskauer has been closely monitoring the quickly-developing legal treatment of AI systems, especially in view of their implications for life sciences patents. AI's presence in life sciences innovation is well established, for example, to predict biological targets of prospective drug molecules and to identify drug design candidates (among many other applications). As we reported in August, two countries--Australia and South Africa--have already permitted AI systems to qualify as "inventors" in patent applications. However, hope for a worldwide trend have been dashed, at least for now.

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Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with self-contained, summary-centric questions and length-constrained, article-summarizing answers. We begin by collecting a new dataset of news articles with questions as titles and pairing them with summaries of varying length. This dataset is used to learn a QA pair generation model producing summaries as answers that balance brevity with sufficiency jointly with their corresponding questions. We then reinforce the QA pair generation process with a differentiable reward function to mitigate exposure bias, a common problem in natural language generation. Both automatic metrics and human evaluation demonstrate these QA pairs successfully capture the central gists of the articles and achieve high answer accuracy.

Driving is an intuitive task that requires skills, constant alertness and vigilance for unexpected events. The driving task also requires long concentration spans focusing on the entire task for prolonged periods, and sophisticated negotiation skills with other road users, including wild animals. These requirements are particularly important when approaching intersections, overtaking, giving way, merging, turning and while adhering to the vast body of road rules. Modern motor vehicles now include an array of smart assistive and autonomous driving systems capable of subsuming some, most, or in limited cases, all of the driving task. The UK Department of Transport's response to the Safe Use of Automated Lane Keeping System consultation proposes that these systems are tested for compliance with relevant traffic rules. Building these smart automotive systems requires software developers with highly technical software engineering skills, and now a lawyer's in-depth knowledge of traffic legislation as well. These skills are required to ensure the systems are able to safely perform their tasks while being observant of the law. This paper presents an approach for deconstructing the complicated legalese of traffic law and representing its requirements and flow. The approach (de)constructs road rules in legal terminology and specifies them in structured English logic that is expressed as Boolean logic for automation and Lawmaps for visualisation. We demonstrate an example using these tools leading to the construction and validation of a Bayesian Network model. We strongly believe these tools to be approachable by programmers and the general public, and capable of use in developing Artificial Intelligence to underpin motor vehicle smart systems, and in validation to ensure these systems are considerate of the law when making decisions.