This paper examines and extends the legal microdirectives Recent research by legal scholars suggests that the law theories in three crucial respects: might inevitably be transformed into legal microdirectives consisting of legal rules that are derived (1) By indicating that legal micro-directives are from legal standards or that are otherwise produced likely to be AIenabled and evolve over time in automatically or via the consequent derivations of scope and velocity across the autonomous levels of legal goals and then propagated via automation for AI Legal Reasoning [20] [22], everyday use as readily accessible lawful directives throughout society. This paper examines and extends (2) By exploring the tradeoffs between legal the legal micro-directives theories in three crucial standards and legal rules as the imprinters of the respects: (1) By indicating that legal micro-directives micro-directives, and are likely to be AIenabled and evolve over time in scope and velocity across the autonomous levels of AI (3) By illuminating a set of brittleness exposures Legal Reasoning, (2) By exploring the tradeoffs that can undermine legal micro-directives and between legal standards and legal rules as the proffering potential mitigating remedies to seek imprinters of the micro-directives, and (3) By greater robustness in the instantiation and illuminating a set of brittleness exposures that can promulgation of such AIenabled lawful directives.

Strategic argumentation provides a simple model of disputation and negotiation among agents. Although agents might be expected to act in our best interests, there is little that enforces such behaviour. (Maher, 2016) introduced a model of corruption and resistance to corruption within strategic argumentation. In this paper we identify corrupt behaviours that are not detected in that formulation. We strengthen the model to detect such behaviours, and show that, under the strengthened model, all the strategic aims in (Maher, 2016) are resistant to corruption.

LONDON – The U.K. government has started consultation on allowing autonomous driving – self-driving – modes up to 70mph, with the potential for legislation as early as spring 2021, bringing U.K. law and standards in line with UNECE targets. The consultation period will run until October 27, 2020, with industry bodies providing expert feedback as well as comments from the public, but it is worth noting this is just the first stage towards autonomous cars on British roads. The consultation is specifically focused on ALKS (Automated Lane Keeping Systems) and operation on motorways, where pedestrians, cyclists and animals should not be a consideration. Rather than the time-limited, hands on the wheel systems you can get currently from premium marques such as Mercedes-Benz and Audi, you'll be able to enjoy the same stress-free approach of autonomous driving as you do with traffic-jam systems at legal motorway speeds. It is highly likely the legislation will proceed – supported by industry bodies such as the AA, who welcome any new safety system – but for it to do so several previous laws must change.

Natural Language Processing is the ability of a Computer program to understand and automatically manipulate the natural language like speech, text etc. According to Wikipedia, Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. The global NLP market size is expected to grow from $10.2 billion in 2019 to $26.4 billion in 2024. The major growth factors of the NLP market include the increase in smart device usage, growth in the adoption of cloud-based solutions and NLP-based applications to improve customer service, as well as the increase in technological investments in the healthcare industry. Legal research lies at the heart of the legal profession.

A historian of technology and race responds to Tochi Onyebuchi's "How to Pay Reparations." Tochi Onyebuchi's "How to Pay Reparations" spoke to me. Its themes rang virtually every note of my twentysomething-year-long career. In 1998, I made my first digital footprint with a signed online petition in support of reparations for the Tulsa race riots. I endured countless run-ins with Oklahoma good ol' boys while crisscrossing the state, working for candidates representing a perpetually losing political party.

This blog post is part of a series where Portulans staff review recent developments in tech policy. Check our Twitter and LinkedIn to follow the conversation. Last month saw the launch of the Oxford Commission on AI and Good Governance, hosted by the Oxford Internet Institute (where our Board Member, Bill Dutton, is a Senior Fellow). Over the next eighteen months, OxCAIGG will contribute research and evidence-based policy recommendations to help governments and public sectors worldwide properly understand and mobilize the power and opportunities granted by machine learning and data science. To this end, the Commission unites world-leading experts on governance, technology, security and human rights.

Is your artificial intelligence fair? Thanks to the increasing adoption of AI, this has become a question that data scientists and legal personnel now routinely confront. Despite the significant resources companies have spent on responsible AI efforts in recent years, organizations still struggle with the day-to-day task of understanding how to operationalize fairness in AI. So what should companies do to steer clear of employing discriminatory algorithms? They can start by looking to a host of legal and statistical precedents for measuring and ensuring algorithmic fairness.

Researchers from the Queensland University of Technology (QUT) in Australia have developed an algorithm that detects misogynistic content on Twitter. The team developed the system by first mining 1 million tweets. They then refined the dataset by searching the posts for three abusive keywords: whore, slut, and rape. Next, they categorized the remaining 5,000 tweets as either misogynistic or not, based on their context and intent. These labeled tweets were then fed to a machine learning classifier, which used the samples to create its own classification model.

One doesn't have to dig too deep into legal organizations to find people who are skeptical about artificial intelligence. AI is getting tremendous attention and significant venture capital, but AI tools frequently underwhelm in the trenches. Here are a few reasons why that is and why I believe GPT-3, a beta version of which was recently released by the OpenAI Foundation, might be a game changer in legal and other knowledge-focused organizations. GPT-3 is getting a lot of oxygen lately because of its size, scope and capabilities. However, it should be recognized that a significant amount of that attention is due to its association with Elon Musk.

Acquisition broadens Fastly's security offering and accelerates Compute@Edge adoption; Expected to be accretive to growth and gross margin Fastly, Inc., provider of an edge cloud platform, announced that it has entered into a definitive agreement to acquire Signal Sciences ("Signal Sciences"), for approximately $775 million in cash and stock. The acquisition will expand Fastly's robust security portfolio at a time when security at the edge has never been more critical. Signal Sciences' strong, developer-first web application and API protection solutions will bolster Fastly's existing security offerings to bring customers a unified edge security solution. Signal Sciences' technology combined with Fastly's current solutions will form Fastly's upcoming new security offering, Secure@Edge. Secure@Edge will be a modern, unified web application and API protection solution that will power and protect companies looking to further or begin their digital transformation.