This is the sixth, and final episode in a series dedicated to all things A.I. In this episode, Tae Royle, Head of Digital Products APAC from Ashurst Advance Digital is joined by Tara Waters, Partner and Head of Ashurst Advance Digital. This is the sixth and final episode in a series dedicated to all things Artificial Intelligence. My name is Tae Royle head of digital products from Ashurst did that digital and today I'm joined by Tara Waters partner and head of Ashurst Advanced Digital based out of our London office. Naturally we come to the question of what's next? In Lewis Carroll's second novel, Alice enters Wonderland by climbing through a mirror.

As part of the MIT Task Force on the Work of the Future's series of research briefs, Professor Thomas Malone, Professor Daniela Rus, and Robert Laubacher collaborated on "Artificial Intelligence and the Future of Work," a brief that provides a comprehensive overview of AI today and what lies at the AI frontier. The authors delve into the question of how work will change with AI and provide policy prescriptions that speak to different parts of society. Thomas Malone is director of the MIT Center for Collective Intelligence and the Patrick J. McGovern Professor of Management in the MIT Sloan School of Management. Daniela Rus is director of the Computer Science and Artificial Intelligence Laboratory, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science, and a member of the MIT Task Force on the Work of the Future. Robert Laubacher is associate director of the MIT Center for Collective Intelligence.

Shalini Kantayya describes herself as a filmmaker who's fascinated with disruptive technologies and the good or harm they create. In a data-driven and increasingly automated world, there's a question of how to protect our civil liberties as artificial intelligence grows by the day. MIT researcher Joy Buolamwini discovered that most facial recognition technology does not see dark-skinned faces and women's faces accurately. This led to an investigation of how the technology we typically see as objective can actually encode racism and sexism. Buolamwini, and others working to change technology for the better around the globe, are featured in Kantayya's documentary Coded Bias.

"Trust is a must," she said. "The EU is spearheading the development of new global norms to make sure AI can be trusted. By setting the standards, we can pave the way to ethical technology worldwide." Any fast-moving technology is likely to create mistrust, but Vestager and her colleagues decreed that those in power should do more to tame AI, partly by using such systems more responsibly and being clearer about how these work. The landmark legislation – designed to "guarantee the safety and fundamental rights of people and businesses, while strengthening AI uptake, investment and innovation" – encourages firms to embrace so-called explainable AI.

Even though we're far from achieving critical mass in the legal profession when it comes to the use of predictive coding technologies and approaches in electronic discovery, the use of predictive coding for document review – especially relevancy review – to support discovery is certainly the most common use of artificial intelligence (AI) and machine learning technologies. Some of you reading this blog post may be "old pros" at this point when it comes to the use of predictive coding while others of you still have yet to "dip your toes" into the predictive coding pool. But applying machine learning technology to support document review (which is predictive coding) is far from the only discovery-related workflow and use case where AI and machine learning technology can be applied. There are several others that forward-thinking organizations are looking to also implement to streamline workflows in the discovery life cycle. How could we forget one of the "forgotten ends" that I discussed last week?

The magnitude of how technology has changed the landscape of the legal profession so far is quite astounding, considering how legal professionals used to be insistent on sticking to the status quo. The digital revolution made practicing law significantly easier by supplying lawyers with tools that streamlined parts of their previously outdated workflow, from researching case files to client relations. As law firms are working remotely and switching to a client-centric approach, now is the right time to consider how the latest IT developments will further impact the legal industry. With clients demanding law firms to be faster, cost-effective, and more flexible, law professionals have embraced automation and law firm software as solutions for growing customer expectations. Automation, in particular, helped law firms save time and deliver more productive results.

AI may benefit or represent a threat to humanity in many ways in numerous fields such as education, environment, health, defense, transportation, space exploration, and so on. To avoid potential drifts of AI and benefit as much as possible from its advantages, AI must be controlled by normative frameworks. Yet, setting legal norms is a difficult and time-consuming process. Therefore, ethics is seen as a convenient and acceptable alternative to laws, since conversely to laws, it is flexible, easily and quickly adjustable, and less constraining than formal rules. The number of Ethics codes that have been issued around the world demonstrates the need to regulate AI while avoiding formal legal constraints.

AI is defined as software that is developed with one or more specified techniques and approaches (including machine learning and deep learning) that can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations or decisions influencing the environments they interact with.

Artificial Intelligence is already part of our lives in the tools and services we use every day. As AI development accelerates, how can authors and small businesses use it as leverage to expand income and opportunities? Ash Fontana gives some ideas in this interview on The AI-First Company.

Although machine learning (ML) is widely used in practice, little is known about practitioners' actual understanding of potential security challenges. In this work, we close this substantial gap in the literature and contribute a qualitative study focusing on developers' mental models of the ML pipeline and potentially vulnerable components. Studying mental models has helped in other security fields to discover root causes or improve risk communication. Our study reveals four characteristic ranges in mental models of industrial practitioners. The first range concerns the intertwined relationship of adversarial machine learning (AML) and classical security. The second range describes structural and functional components. The third range expresses individual variations of mental models, which are neither explained by the application nor by the educational background of the corresponding subjects. The fourth range corresponds to the varying levels of technical depth, which are however not determined by our subjects' level of knowledge. Our characteristic ranges have implications for the integration of AML into corporate workflows, security enhancing tools for practitioners, and creating appropriate regulatory frameworks for AML.