The American Civil Liberties Union (ACLU) is pressing forward with a lawsuit involving the facial recognition software offered by Amazon and Microsoft to government clients. In a complaint filed in a Massachusetts federal court, the ACLU asked for a variety of different records from the government, including inquiries to companies, meetings about the piloting or testing of facial recognition, voice recognition, and gait recognition technology, requests for proposals, and licensing agreements. At the heart of the lawsuit are Amazon's Rekognition and Microsoft's Face API, both facial recognition products that are available for customers of the companies' cloud platforms. The ACLU has also asked for more details on the US government's use of voice recognition and gait recognition, which is the automated process of comparing images of the way a person walks in order to identify them. Police in Shanghai and Beijing are already using gait-analysis tools to identify people.

Do you have two left feet, but wish that, just once, you could bust some moves -- or at least look like you can? Good news: There's now an AI for that, and it needs just one full body photo to make you look like the next Beyoncé. The AI is the work of NVIDIA's research team. They recently published a paper on the tech on the pre-print server arXiv, and they've pledged to release the code as soon as lawyers "resolve some legal issues." In addition to working on people, the AI can make sculptures look like they're dancing their asses off, too. It can also be applied to just faces, making a person look like they're saying something they didn't. And if that wasn't enough, it can also take an image of any city and output a video of a car seemingly driving down its streets.

A clampdown on video face recognition systems is spreading around major cities, as politicians police and others continue to argue its merits. Meanwhile, a tightening of rules on other AI and smart technologies is on the way. Chatbots are the latest to feel the effect with a Californian law banning bots that fail to identify themselves as such. When any new technology arrives, there are people who want pragmatic laws to protect users and bystanders – think the red flags that had to be waved in front of early cars. There are also crazy laws passed to try and protect vested interests or big business. Look at all the roadblocks thrown in front of solar power and electric cars to keep big oil happy.

IBM weighed in Nov 5 on the policy debate over facial recognition technology, arguing against an outright ban but calling for "precision regulation" to protect privacy and civil liberties. In a white paper posted on its website, the US computing giant said policymakers should understand that "not all technology lumped under the umbrella of'facial recognition' is the same". IBM said uneasiness about artificial intelligence technology which can use face scans for identification was reasonable. "However, blanket bans on technology are not the answer to concerns around specific use cases," said the paper by IBM chief privacy officer Christina Montgomery and Ryan Hagemann, co-director the IBM policy lab. "Casting such a wide regulatory net runs the very real risk of cutting us off from the many – and potentially life-saving – benefits these technologies offer."

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Despite the recent surge of interest in designing and guaranteeing mathematical formulations of fairness, virtually all existing notions of algorithmic fairness fail to be adaptable to the intricacies and nuances of the decision-making context at hand. We argue that capturing such factors is an inherently human task, as it requires knowledge of the social background in which machine learning tools impact real people's outcomes and a deep understanding of the ramifications of automated decisions for decision subjects and society. In this work, we present a framework to construct a context-dependent mathematical formulation of fairness utilizing people's judgment of fairness. We utilize the theoretical model of Heidari et al. (2019)---which shows that most existing formulations of algorithmic fairness are special cases of economic models of Equality of Opportunity (EOP)---and present a practical human-in-the-loop approach to pinpoint the fairness notion in the EOP family that best captures people's perception of fairness in the given context. To illustrate our framework, we run human-subject experiments designed to learn the parameters of Heidari et al.'s EOP model (including circumstance, desert, and utility) in a hypothetical recidivism decision-making scenario. Our work takes an initial step toward democratizing the formulation of fairness and utilizing human-judgment to tackle a fundamental shortcoming of automated decision-making systems: that the machine on its own is incapable of understanding and processing the human aspects and social context of its decisions.

A prominent rights group is urging the Federal Trade Commission to take on the recruiting-technology company HireVue, arguing the firm has turned to unfair and deceptive trade practices in its use of face-scanning technology to assess job candidates' "employability." The Electronic Privacy Information Center, known as EPIC, on Wednesday filed an official complaint calling on the FTC to investigate HireVue's business practices, saying the company's use of unproven artificial-intelligence systems that scan people's faces and voices constituted a wide-scale threat to American workers. HireVue's "AI-driven assessments," which more than 100 employers have used on more than a million job candidates, use video interviews to analyze hundreds of thousands of data points related to a person's speaking voice, word selection and facial movements. The system then creates a computer-generated estimate of the candidates' skills and behaviors, including their "willingness to learn" and "personal stability." Candidates aren't told their scores, but employers can use those reports to decide whom to hire or disregard. The the Utah-based company was the subject of a Washington Post report last month, in which AI researchers criticized its technology as "profoundly disturbing" and "opaque."

We examine the impacts of different proposed AI regulations on managers' intentions to adopt AI technologies and on their AI-related business strategies. We conduct a randomized online survey experiment on more than a thousand managers in the U.S. We randomly present managers with different proposed AI regulations, and ask them to make decisions about AI adoption, budget allocation, hiring, and other issues. We have four main findings: (1) information about AI regulation generally reduces the rate of adoption of AI technologies. Nonetheless, industry- and agency-specific AI regulation has a smaller impact than general AI regulation. That is, firms spend more on developing AI strategy and hire more managers.

Forward-Looking Safe Harbor Statement This press release contains certain forward-looking statements, as that term is defined in the Private Securities Litigation Reform Act of 1995, including those relating to the products and services described herein and to fiscal 2020 business performance and beyond and the Company's plans for growth and improvement in profitability and cash flow. You can identify these statements by the use of the words "may," "will," "could," "should," "would," "plans," "expects," "anticipates," "continue," "estimate," "project," "intend," "likely," "forecast," "probable," "potential," and similar expressions. These forward-looking statements involve risks and uncertainties that could cause actual results to differ materially from those projected or anticipated. Such risks and uncertainties include, but are not limited to, continued funding of defense programs, the timing and amounts of such funding, general economic and business conditions, including unforeseen weakness in the Company's markets, effects of any U.S. Federal government shutdown or extended continuing resolution, effects of continued geopolitical unrest and regional conflicts, competition, changes in technology and methods of marketing, delays in completing engineering and manufacturing programs, changes in customer order patterns, changes in product mix, continued success in technological advances and delivering technological innovations, changes in, or in the U.S. Government's interpretation of, federal export control or procurement rules and regulations, market acceptance of the Company's products, shortages in components, production delays or unanticipated expenses due to performance quality issues with outsourced components, inability to fully realize the expected benefits from acquisitions and restructurings, or delays in realizing such benefits, challenges in integrating acquired businesses and achieving anticipated synergies, increases in interest rates, changes to cyber-security regulations and requirements, changes in tax rates or tax regulations, changes to interest rate swaps or other cash flow hedging arrangements, changes to generally accepted accounting principles, difficulties in retaining key employees and customers, unanticipated costs under fixed-price service and system integration engagements, and various other factors beyond our control. These risks and uncertainties also include such additional risk factors as are discussed in the Company's filings with the U.S. Securities and Exchange Commission, including its Annual Report on Form 10-K for the fiscal year ended June 30, 2019.

A law firm chatbot is an integral legal tech tool designed to assist perspective clients, clients, and those who work in a law office. Of course, exactly what the chatbot does depends on how it is created. They can be designed to handle everything from interacting with website visitors to assisting with certain law office automation tasks such as taking input and creating commonly used legal forms. While chatbots can be a life changing tool for the law office. The catalyst for determining whether the change is positive of negative depends on avoiding common mistakes.