This article is part of the Policing and Technology Project, a collaboration between Future Tense and the Tech, Law, & Security Program at American University Washington College of Law that examines the relationship between law enforcement, police reform, and technology. It's the middle of the night, you are disoriented, and they want to know where you were earlier in the day. You have no idea at that moment that your ex-girlfriend was found dead, and some of your fingerprints were found at her house--but you do know you have the right to remain silent. Until the cops bring out the headset. One of the hallmarks of the U.S. Constitution is the enumerated right of citizens to not be coerced into self-incrimination or be allowed to "take the Fifth."

Non-disclosure agreements (NDAs) are some of the most commonly drafted agreements at any company. While they may be common, however, that doesn't mean they're unimportant – in fact, they're critical to protecting a company's business strategies and trade secrets. Most companies use the same form NDA in almost every situation, changing only party names and the description of the confidential information involved, leaving the rest of the agreement to a series of standard terms. This means that, even though they're important, NDAs are very repetitive and routine in terms of drafting. Corporate legal departments have long been bogged down in routine contracts. Preparing NDAs can take up a significant amount of lawyers' time, taking them away from other important work that can bring more value to the organization.

Over the decades, fashions in Computational Linguistics have changed again and again, with major shifts in motivations, methods and applications. When digital computers first appeared, linguistic analysis adopted the new methods of information theory, which accorded well with the ideas that dominated psychology and philosophy. Then came formal language theory and the idea of AI as applied logic, in sync with the development of cognitive science. That was followed by a revival of 1950s-style empiricism—AI as applied statistics—which in turn was followed by the age of deep nets. There are signs that the climate is changing again, and we offer some thoughts about paths forward, especially for younger researchers who will soon be the leaders.

Welcome to the latest Artificial Lawyer Product Review. Today we are presenting Part One of the results for Luminance. Luminance is a UK-based legal AI doc review and analysis company, and in a short time has become one of the most well-known brands in this category. It has grown rapidly in terms of its offering (see image below) and in terms of its global client base, with now around 170 customers noted by the company. What started as an M&A due diligence tool, mostly focused on law firms, has broadened to cover a wider range of legal practice needs, such as property and compliance.

Despite the a wave of national strategies on artificial intelligence that has washed over the world, none have yet proposed or published specific ethical or legal frameworks for artificial intelligence. Over the past several years, a wave of national strategies on artificial intelligence (AI) has washed over the world, with many jurisdictions introducing policies for its regulation. With the exception of the European Union (EU), none have yet proposed or published specific ethical or legal frameworks for AI. Canada led the way, announcing national AI policies in 2017, and has since been followed by many other jurisdictions. The Organization for Economic Co-operation and Development (OECD) AI Policy Observatory early last year released a continuously updated database of over 600 AI policy initiatives from 60 countries, territories, and the EU. Of course, not all are the same, but some are noteworthy.

Privacy groups in Europe have filed complaints against Clearview AI for allegedly breaking privacy laws by scraping billions of photos from social media sites like Facebook, Bloomberg has reported. Watchdog groups like Privacy International have taken legal action against the company in Austria, France, Greece, Italy and the UK, telling regulators that the practices "are incredibly invasive and dangerous." "Extracting our unique facial features or even sharing them with the police and other companies goes far beyond what we could ever expect as online users," Privacy International's Ioannis Kouvakas told Bloomberg. Clearview has been controversial since it was first revealed. The company has an immense database of faces taken from social media and uses AI to compare those to images from security cameras or other sources.

A recent patent application has proposed a system for identifying'aggressive driving behavior' at junctions using machine learning algorithms deployed in civic edge computing devices. In contrast to recent innovations of AI research into in-vehicle'road rage' analytics (primarily intended for the benefit of insurance companies), the system proposed is instead municipal in nature, and could be aimed at facilitating penalties for drivers that are not conforming to the ambient norms of'safe' driver behavior. It is also specifically intended to provide bad drivers with related in-car audiovisual alerts. The patent was filed at the US Patent and Trademark Office on 29th April 2021 on behalf of the Board Of Regents of the University of Michigan, and the Denso corporation, a Japanese automotive components manufacturer owned by Toyota. The UMich patent is not a proprietary, in-car system aimed at insurance oversight, nor designed solely to produce forensic data, but rather relies on well-resourced edge computing nodes deployed at traffic intersections to provide immediate and actionable feedback, by collating data from roadside edge computing resources and from sensors installed in nearby vehicles.

Nearly two-thirds of C-level AI leaders can't explain how specific AI decisions or predictions are made, according to a new survey on AI ethics by FICO, which says there is room for improvement. FICO hired Corinium to query 100 AI leaders for its new study, called "The State of Responsible AI: 2021," which the credit report company released today. More than two thirds of survey-takers say the processes they have to ensure AI models comply with regulations are ineffective, while nine out of 10 leaders who took the survey say inefficient monitoring of models presents a barrier to AI adoption. Seeing as how the regulatory environment is still developing, it's concerning that 43% of respondents in FICO's study found that "they have no responsibilities beyond meeting regulatory compliance to ethically manage AI systems whose decisions may indirectly affect people's livelihoods," such as audience segmentation models, facial recognition models, and recommendation systems, the company said. At a time when AI is making life-altering decisions for their customers and stakeholders, the lack of awareness of the ethical and fairness concerns around AI poses a serious risk to companies, says Scott Zoldi, FICO's chief analytics officer.

Expectations are high, but application is yet to come to fruition. We're talking about artificial intelligence (AI) and machine learning, as discussed in the 2020-2021 World Quality Report from Capgemini and Sogeti, in partnership with Micro Focus, published on November 5, 2020. There's a general buzz of excitement at the potential for using AI and machine learning in quality assurance (QA) and testing, just as there was last year. Yet, while our WQR survey findings reveal some evidence of supervised learning as a core part of machine learning (ML) in making quality engineering smarter, we're not seeing the required maturity to show visible results. Several questions arise for those of us watching the evolution of AI and ML in quality assurance.

The seminar is a preparatory event for ICAIL 2021, with introductory talks on AI & Law. Four of the leading experts on the field have confirmed presence.