Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward - it is to design models that are inherently interpretable.

The Artificial Intelligence (AI) revolution foretold of during the 1960s is well underway in the second decade of the 21st century. Its period of phenomenal growth likely lies ahead. Still, we believe, there are crucial lessons that biology can offer that will enable a prosperous future for AI. For machines in general, and for AI's especially, operating over extended periods or in extreme environments will require energy usage orders of magnitudes more efficient than exists today. In many operational environments, energy sources will be constrained. Any plans for AI devices operating in a challenging environment must begin with the question of how they are powered, where fuel is located, how energy is stored and made available to the machine, and how long the machine can operate on specific energy units. Hence, the materials and technologies that provide the needed energy represent a critical challenge towards future use-scenarios of AI and should be integrated into their design. Here we make four recommendations for stakeholders and especially decision makers to facilitate a successful trajectory for this technology. First, that scientific societies and governments coordinate Biomimetic Research for Energy-efficient, AI Designs (BREAD); a multinational initiative and a funding strategy for investments in the future integrated design of energetics into AI. Second, that biomimetic energetic solutions be central to design consideration for future AI. Third, that a pre-competitive space be organized between stakeholder partners and fourth, that a trainee pipeline be established to ensure the human capital required for success in this area.

Quantitative definitions of what is unfair and what is fair have been introduced in multiple disciplines for well over 50 years, including in education, hiring, and machine learning. We trace how the notion of fairness has been defined within the testing communities of education and hiring over the past half century, exploring the cultural and social context in which different fairness definitions have emerged. In some cases, earlier definitions of fairness are similar or identical to definitions of fairness in current machine learning research, and foreshadow current formal work. In other cases, insights into what fairness means and how to measure it have largely gone overlooked. We compare past and current notions of fairness along several dimensions, including the fairness criteria, the focus of the criteria (e.g., a test, a model, or its use), the relationship of fairness to individuals, groups, and subgroups, and the mathematical method for measuring fairness (e.g., classification, regression). This work points the way towards future research and measurement of (un)fairness that builds from our modern understanding of fairness while incorporating insights from the past.

Artificial intelligence (AI) might be technology's Holy Grail, but Australia's Human Rights Commissioner Edward Santow has warned about the need for responsible innovation and an understanding of the challenges new technology poses for basic human rights. "AI is enabling breakthroughs right now: Healthcare, robotics, and manufacturing; pretty soon we're told AI will bring us everything from the perfect dating algorithm to interstellar travel -- it's easy in other words to get carried away, yet we should remember AI is still in its infancy," Santow told the Human Rights & Technology conference in Sydney in July. Santow was launching the Human Rights and Technology Issues Paper, which was described as the beginning of a major project by the Human Rights Commission to protect the rights of Australians in a new era of technological change. The paper [PDF] poses questions centred on what protections are needed when AI is used in decisions that affect the basic rights of people. It asks also what is required from lawmakers, governments, researchers, developers, and tech companies big and small. Pointing to Microsoft's AI Twitter bot Tay, which in March 2016 showed the ugly side of humanity -- at least as present on social media -- Santow said it is a key example of how AI must be right before it's unleashed onto humans.

Since the first version of this white paper in 2016, the range and impact of Artificial Intelligence (AI) has expanded at a dizzying pace as the area continues to capture an ever greater share of the business and popular imaginations. Along with the cloud, AI is emerging as the key driver of the'fourth industrial revolution', the term (after steam, electricity and computing) coined by Davos founder Klaus Schwab for the deep digital transformation now under way. This white paper is written from the perspective of the in-house lawyer working on the legal aspects of their organisation's adoption and use of AI. "artificial intelligence is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment".4 "interdisciplinary field ... dealing with models and systems for the performance of functions generally associated with human intelligence, such as reasoning and learning." Most recently, in its January 2018 book, 'The Future: Computed', Microsoft thinks of AI as: "a set of technologies that enable computers to perceive, learn, reason and assist in decision- making to solve problems in ways that are similar to what people do."7

As machine learning systems are increasingly used to make real world legal and financial decisions, it is of paramount importance that we develop algorithms to verify that these systems do not discriminate against minorities. We design a scalable algorithm for verifying fairness specifications. Our algorithm obtains strong correctness guarantees based on adaptive concentration inequalities; such inequalities enable our algorithm to adaptively take samples until it has enough data to make a decision. We implement our algorithm in a tool called VeriFair, and show that it scales to large machine learning models, including a deep recurrent neural network that is more than five orders of magnitude larger than the largest previously-verified neural network. While our technique only gives probabilistic guarantees due to the use of random samples, we show that we can choose the probability of error to be extremely small.

The first time many of us heard about China's use of facial recognition on jaywalkers was just this week when a prominent Chinese businesswoman was publicly "named and shamed" for improper street crossing. Turns out, she wasn't even there: China's terrifyingly over-the-top use of tech for citizen surveillance made a mistake. The AI system identified Dong Mingzhu's face from a bus advertisement for her company's products. "[The] president of China's biggest air conditioning maker," wrote The Telegraph, "had her image flashed up on a public display screen in the city of Ningbo, near Shanghai, with a caption saying she had illegally crossed the street on a red light." Shortly after, Ningbo traffic police admitted the mistake and claimed to have "completely upgraded the system to reduce the false recognition rate."

Investment in defences against cyberattacks has overtaken artificial intelligence as the main technology issue for law firms, a report reveals today. Cybersecurity was the area cited most often by leaders of the UK's biggest law firms when researchers asked how they were allocating their technology budgets. Artificial intelligence, broadly seen as the must-have technology function over the last 12 months, was relegated to fourth place on the investment league table. Law firm leaders at the top-50 practices in the country cited "client collaboration tools" -- software packages that share information between in-house lawyers and law firms -- and automated document production as being more important that AI.

Virtual assistants such as Amazon's Alexa and Google Home have the capacity to analyse how happy and healthy a couple's relationship is, research has found. In-home listening devices will soon be able to judge how functional relationships are as well as interrupt an argument with an idea for how to resolve it, the study said. The research, by Imperial College Business School, stated that within the next two to three years, digital assistants could predict with 75 per cent accuracy the likelihood of a relationship or marriage being a success. The technology would reach a verdict through acoustic analysis of communication between couples – examining everything from everyday encounters to arguments. The virtual assistants would then be able to provide relationship advice and what researchers refer to as democratising counselling.

LAST week, regulators in the US announced plans to review export controls across a wide range of emerging technologies. The list includes artificial intelligence and machine learning, as well as technologies that would make substantial use of AI and machine learning, such as robotics and brain-computer interfaces, and supporting technologies. This may result in the US becoming the first nation to explicitly control the spread of AI technologies. Law-makers are responsible for balancing economic prosperity and growth against public safety threats.