MIT has terminated a research collaboration with iFlytek, a Chinese artificial intelligence company accused of supplying technology for surveilling Muslims in the northwestern province of Xinjiang. The university canceled the relationship in February after reviewing an upcoming project under tightened guidelines governing funding from companies in China, Russia, and Saudi Arabia. MIT has not said why it terminated the iFlytek collaboration or disclosed details about the project that prompted the review, but it has faced pushback from some students and staff about the arrangement since it began two years ago. "We take very seriously concerns about national security and economic security threats from China and other countries, and human rights issues," says Maria Zuber, vice president of research at MIT. US companies and universities have built ties with Chinese tech firms in recent years. But the relationships have come under increasing scrutiny as relations between the two countries have soured.

The last decade has seen a vast increase both in the diversity of applications to which machine learning is applied, and to the import of those applications. Machine learning is no longer just the engine behind ad placements and spam filters; it is now used to filter loan applicants, deploy police officers, and inform bail and parole decisions, among other things. The result has been a major concern for the potential for data-driven methods to introduce and perpetuate discriminatory practices, and to otherwise be unfair. And this concern has not been without reason: a steady stream of empirical findings has shown that data-driven methods can unintentionally both encode existing human biases and introduce new ones.7,9,11,60 At the same time, the last two years have seen an unprecedented explosion in interest from the academic community in studying fairness and machine learning. "Fairness and transparency" transformed from a niche topic with a trickle of papers produced every year (at least since the work of Pedresh56 to a major subfield of machine learning, complete with a dedicated archival conference--ACM FAT*). But despite the volume and velocity of published work, our understanding of the fundamental questions related to fairness and machine learning remain in its infancy.

Sandra Aistars is a Clinical Professor at Antonin Scalia Law School, George Mason University, leading the law school's Arts & Entertainment Advocacy Program. Throughout her career she has served in positions that required mastery of intellectual property issues, federal policy process and development, and the ability to understand and manage the implications of intellectual property policies across a portfolio of businesses. In addition, Aistars has a wealth of experience working with policy makers in Washington and internationally. She has served on trade missions and been an industry advisor to the Department of Commerce on intellectual property implications for international trade negotiations; worked on legislative and regulatory matters worldwide; frequently testified before Congress and federal agencies regarding intellectual property matters; chaired cross-industry coalitions and technology standards efforts; and is regularly tapped by government agencies to lecture in U.S. government-sponsored study tours for visiting legislators, judges, prosecutors, and regulators. Aistars has also previously served as Vice President and Associate General Counsel at Time Warner Inc.

The debate over the police using machine learning is intensifying – it is considered in some quarters as controversial as stop and search. Stop and search is one of the most contentious areas of how the police interact with the public. It has been heavily criticized for being discriminatory towards black and minority ethnic groups, and for having marginal effects on reducing crime. In the same way, the police use of machine learning algorithms has been condemned by human rights groups who claim such programs encourage racial profiling and discrimination along with threatening privacy and freedom of expression. Broadly speaking, machine learning uses data to teach computers to make decisions without explicitly instructing them how to do it.

Today, we see how computer programs, algorithms, and robots replace simple human activities, but there is the technology that is at the forefront of the spectrum: AI. The consequences of artificial intelligence have such an impact that they incite us to wonder if we are experiencing the beginning of a new era. According to Gartner, business use of AI has grown by 270% in the last four years, and slightly more than a third of organizations have implemented AI in some way, according to their specific needs. But is this also a reality within the legal sector? AI has found its way to support attorneys and clients alike, and there is a clear growing interest in technology.

Continuing efforts to have a robust corporate governance system and ensure high level of compliance, the ministry is also in the process of having an advanced MCA 21 portal. The portal is used for submission of requisite filings under the companies law and is also a repository of data on corporates in the country. Corporate Affairs Secretary Injeti Srinivas told PTI that once the third version of MCA 21 becomes fully operational, the portal would make it "almost impossible for a shell company to survive." Generally, shell companies are those which are not complying with regulations and many such entities are allegedly used for money laundering and other illegal activities. Noting that the third version of the portal might be fully operational in a year from now, the secretary said the ecosystem would have zero tolerance for non-compliance.

Companies using artificial intelligence (AI) across their business units should consider creating a C-suite position to oversee how AI is used and guard against the risk of making bad decisions based on biased algorithms, experts say. Only a few companies, like Levi Strauss & Co, have established a chief artificial intelligence officer (CAIO) position, and fewer have created a C-level position dedicated solely to AI ethics. Brian Kropp, chief of research in the HR practice at Gartner, said chief technology officers and chief information officers will struggle with handling AI-related decisions and ethical dilemmas. "CTOs and CIOs are going to be thinking about the role through the lens of how they can make the technology work," Kropp said. However, "artificial intelligence is not a question of how you get the technology to work; it's a question of how do you think through the implications of the technology?"

Abstract: The recent enthusiasm for artificial intelligence (AI) is due principally to advances in deep learning. Deep learning methods are remarkably accurate, but also opaque, which limits their potential use in safety-critical applications. To achieve trust and accountability, designers and operators of machine learning algorithms must be able to explain the inner workings, the results and the causes of failures of algorithms to users, regulators, and citizens. The originality of this paper is to combine technical, legal and economic aspects of explainability to develop a framework for defining the "right" level of explain-ability in a given context. We propose three logical steps: First, define the main contextual factors, such as who the audience of the explanation is, the operational context, the level of harm that the system could cause, and the legal/regulatory framework.

The Patent Office recently launched a page for artificial intelligence information on its website. The page provides information on the Patent Office's AI initiatives, public notices and responses, AI-related events and outside resources. The website is a part of a broader effort by the Patent Office to engage with the innovation community and experts on issues relating to AI. Director Iancu stated the following: One of the agency's top priorities is to ensure that the United States maintains its leadership in innovation, especially in emerging technologies such as artificial intelligence (AI). To that end, the USPTO has been actively engaging with the innovation community and experts in AI to determine whether further guidance is needed to promote the predictability and reliability of intellectual property rights relating to AI technology and to encourage further innovation in and around this critical area. The Patent Office has solicited comments relating to AI issues and has received nearly 200 responses from individuals, corporations, associations, academia and others.

To see the'Sheet book' and'Table of associated records' go the bottom, Pag. 2, Pag. 3. ( For free online version, on the image). Who might face civil liability if autonomous vehicles (AVs) are hacked to steal data or inflict mayhem, injuries, and damage? How will the civil justice and insurance systems adjust to handle such claims? RAND researchers addressed these questions to help those in the automotive, technology, legal, and insurance industries prepare for the shifting roles and responsibilities that the era of AVs may bring. Using four scenarios (a ransomware attack, a hacked vehicle damaging government property, hacks on a connected roadway that cause damage, and theft of information through hacking of AVs), the authors explored the civil legal theories that may come into play when real-world damages result from AVs being hacked.