European Law now requires AI to be explainable in the context of adverse decisions affecting European Union (EU) citizens. At the same time, it is expected that there will be increasing instances of AI failure as it operates on imperfect data. This paper puts forward a neurally-inspired framework called decision stacks that can provide for a way forward in research aimed at developing explainable AI. Leveraging findings from memory systems in biological brains, the decision stack framework operationalizes the definition of explainability and then proposes a test that can potentially reveal how a given AI decision came to its conclusion.

Billing clients is a time-consuming task that unfortunately adds to an attorney's already busy workload. Unlike many other tasks, billing is not a task an attorney can bill for. All the time spent dealing with time entry and invoicing could be going toward billable activities, or to the attorney's free time. The Un-Billable Hour, a law practice and advisory podcast, confirms that inefficiencies related to un-billable tasks cost law firms money. The podcasts specifically addresses "disorganization, slow response to clients, late billing invoices and not tracking time" as the principal causes of decreased efficiency and lower revenue. Although there are many applications that at least claim to streamline the time-entry part of the billing process, these same applications typically neglect the bill review portion of bill submission.

We're releasing the 774 million parameter GPT-2 language model after the release of our small 124M model in February, staged release of our medium 355M model in May, and subsequent research with partners and the AI community into the model's potential for misuse and societal benefit. We're also releasing an open-source legal agreement to make it easier for organizations to initiate model-sharing partnerships with each other, and are publishing a technical report about our experience in coordinating with the wider AI research community on publication norms. To date, there hasn't been a public release of a 1558M parameter language model, though multiple organizations have developed the systems to train them, or have publicly discussed how to train larger models. For example, teams from both NLP developer Hugging Face and the Allen Institute for Artificial Intelligence (AI2) with the University of Washington have explicitly adopted similar staged release approaches to us. Since February, we've spoken with more than five groups who have replicated GPT-2[1].

Facial recognition technology is spreading fast. Already widespread in China, software that identifies people by comparing images of their faces against a database of records is now being adopted across much of the rest of the world. It's common among police forces but has also been used at airports, railway stations and shopping centres. The rapid growth of this technology has triggered a much-needed debate. Activists, politicians, academics and even police forces are expressing serious concerns over the impact facial recognition could have on a political culture based on rights and democracy. As someone who researches the future of human rights, I share these concerns.

The guide gives an overview of how the AI industry is developing in Ireland as well as how EU guidance and regulation is shaping up. The European Commission is proposing a regulatory environment based on a strong legal and ethical framework including key elements such as human agency and oversight, technical robustness and safety, privacy and data governance as well as transparency. During the course of its review, the EU has also considered other interesting issues such as whether it will be necessary to create a specific legal status for robots with a view to making them electronic persons with rights and responsibilities.

New York Gov. Andrew Cuomo signed legislation in late July to create a temporary state commission that will examine how artificial intelligence impacts his state. In doing so, New York joined Vermont, Alabama, and Washington in establishing an A.I. task force that will examine the cutting-edge technology and then make recommendations about how it should be regulated. The groups vary in their mission, but the general message is the same: companies pushing A.I., the brains behind innovation like robotics and facial recognition software, can't necessarily be trusted to do what's in the best interest of state residents. Brandie Nonnecke, founding director of University of California's Center for Information Technology Research in the Interest of Society Policy Lab, says that task forces could help keep state lawmakers up to date about the technology. The end result, she says, will be better-written bills that don't get stuck in legislative purgatory.

Perhaps the most frequent objection to the development of artificial intelligence is the lack of certainty over whether such a powerful innovation will genuinely be good for humanity. Who is responsible if an AI-powered device harms someone? How will we ever know whether AI can behave morally? Where should AI be put to use, and where is it better off left out? These are fundamental questions that yield many different answers from experts in the field of Artificial Intelligence Ethics. This field, which has exploded because of the rapid growth of artificial intelligence, explores the philosophical issues posed by this new technology.

As the seat of the first Industrial Revolution, the UK has a long history of designing regulatory solutions to the challenges posed by technological change. However, regulation has often lagged behind - sometimes very far behind - new technology. Artificial Intelligence (AI) is proving no exception to this historical trend. In the first place, there is currently no consensus on whether the development of AI requires its own dedicated regulator or specific statutory regime. Gathering evidence for its May 2018 report "AI in the UK", the Select Committee on AI of the House of Lords found that opinions were divided into three camps: "those who considered existing laws could do the job; those who thought that action was needed immediately; and those who proposed a more cautious and staged approach to regulation"[1]. The first of these categories - where it was argued that existing laws were sufficient - included strong interest groups such as TechUK (a major trade association) and the Law Society of England and Wales.

Underlying cause of death coding from death certificates is a process that is nowadays undertaken mostly by humans with a potential assistance from expert systems such as the Iris software. It is as a consequence an expensive process that can in addition suffer from geospatial discrepancies, thus severely impairing the comparability of death statistics at the international level. The recent advances in artificial intelligence, specifically the raise of deep learning methods, has enabled computers to make efficient decisions on a number of complex problem that were typically considered as out of reach without human assistance. They however require a considerable amount of data to learn from, which is typically their main limiting factor. However, the C\'epiDc stores an exhaustive database of death certificate at the French national scale, amounting to several millions training example available for the machine learning practitioner. This article presents a deep learning based tool for automated coding of the underlying cause of death from the data contained in death certificates with 97.8% accuracy, a substantial achievement compared to the Iris software and its 75% accuracy assessed on the same test examples. Such an improvement opens a whole field of new applications, from nosologist-level batch automated coding to international and temporal harmonization of cause of death statistics.

As it is, the world is unfair. The question now is, do we want automated tech to be unfair too? As we build more and more AI-dependent smart digital infrastructure in our cities and beyond, we have pretty much overlooked the emerging character of artificial intelligence that would have a profound bearing on our nature and future. Are we happy with algorithms making decisions for us? Naturally, one would expect the algorithm to possess discretion.