Legaltech news recently explored findings of the American Bar Association's Legal Technology Survey Report, which showed hesitancy – particularly among smaller firms – to adopt artificial intelligence (AI) tools. Accuracy and reliability were survey respondents' top two concerns around AI. Their reluctance to embrace new technologies may be understandable. New tools can be intimidating, especially for leaders of small firms and solo practices who – like most lawyers – don't have backgrounds in technology or science. Yet small firm leaders arguably stand to benefit the most from new technologies like AI, which can help them manage their practice and business more efficiently and effectively. The day-to-day challenges of practicing law in small firms – from business development to spending too much time on administrative tasks – are precisely those that AI and legal tech can address. These challenges and other issues, along with some suggested guidance on a path forward are discussed in the 2019 State of the U.S. Small Law Firms Report.

A leading expert in artificial intelligence has issued a stark warning against the use of race- and gender-biased algorithms for making critical decisions. Across the globe, algorithms are beginning to oversee various processes from job applications and immigration requests to bail terms and welfare applications. Military researchers are even exploring whether facial recognition technology could enable autonomous drones to identify their own targets. However, University of Sheffield computer expert Noel Sharkey told the Guardian that such algorithms are'infected with biases' and cannot be trusted. Calling for a halt on all AI with the potential to change people's lives, Professor Sharkey instead advocates for vigorous testing before they are used in public.

Advances in Artificial Intelligence (AI), Machine Learning (ML) and data science are rekindling interest in applying computation to more aspects of legal process and decision-making. This is particularly evident through the development of various AI-leveraging LegalTech applications to assist with legal practice and business, law enforcement, and the prediction of case outcomes, among other things. The use of algorithmic decision-making (ADM) systems to replicate, and in some cases: replace, human judges and other decision-makers has, however, preoccupied the attention of the public, media, and scholars. Powles and Nissenbaum suggest that the'seductive diversion' of solving the'bias problem' makes the totalisation of AI in society contingent on solving narrow computational puzzles and'ethics washing' away hard questions, bad business practices and worse ideas. Not more fundamental questions about the compatibility of autonomous systems with the rule of law, deliberative democracy, and ultimately: should we be building them at all?

The World Intellectual Property Organization (WIPO) today launched a public consultation process on artificial intelligence (AI) and intellectual property (IP) policy, inviting feedback on an issues paper designed to help define the most-pressing questions likely to face IP policy makers as AI increases in importance. Beginning December 13, 2019, WIPO published its issues paper with a call for comments from the widest-possible global audience. It is the latest step in WIPO's response to the ongoing interaction of AI with the IP system, including the use of AI applications in IP administration. "Artificial intelligence is set to radically alter the way in which we work and live, with great potential to help us solve common global challenges, but it is also prompting policy questions and challenges," said WIPO Director General Francis Gurry. Machine learning relies on information in the form of electronic data, which is also at the heart of intellectual property and innovation in a global digital economy.

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There's little doubt that artificial intelligence (AI) is having a massive impact on IT budgets, operations, and user experiences. But an area of AI that is receiving increasing attention is ethics. As people and companies become more dependent on the use of algorithms to make and support decisions, the inherent biases of software developers and the data pools they depend on to build their models have come under close scrutiny. Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of them.

In a recent McKinsey survey, only 13 percent of CFOs and other senior business executives polled said their finance organizations use automation technologies, such as robotic process automation (RPA) and machine learning. What's more, when asked how much return on investment the finance organization has generated from digitization and automation in the past 12 months, only 5 percent said it was a substantial return; the more common response was "modest" or "minimal" returns. While that number may seem low right now, automation is coming to the finance function, and it will play a crucial role in furthering the CFO's position in the C-suite. Research suggests corporate finance teams spend about 80 percent of their time manually gathering, verifying, and consolidating data, leaving only about 20 percent for higher-level tasks, such as analysis and decision-making. In its truest form, RPA will unleash a new wave of digital transformation in corporate finance.

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Artificial intelligence (AI) will shape the destiny of humanity, but first humanity has the opportunity to shape AI. At times, anxiety about the former causes us to overlook the latter. We forget that artificial intelligence is going to serve the goals with which we're now programming it. Its implementation will follow the standards we now have the opportunity to set. This leaves citizens -- and those representing us in government -- with an urgent responsibility.

In recent years, interest in monitoring air quality has been growing. Traditional environmental monitoring stations are very expensive, both to acquire and to maintain, therefore their deployment is generally very sparse. This is a problem when trying to generate air quality maps with a fine spatial resolution. Given the general interest in air quality monitoring, low-cost air quality sensors have become an active area of research and development. Low-cost air quality sensors can be deployed at a finer level of granularity than traditional monitoring stations. Furthermore, they can be portable and mobile. Low-cost air quality sensors, however, present some challenges: they suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Some promising machine learning approaches can help us obtain highly accurate measurements with low-cost air quality sensors. In this article, we present low-cost sensor technologies, and we survey and assess machine learning-based calibration techniques for their calibration. We conclude by presenting open questions and directions for future research.