A New Jersey man is suing the town of Woodbridge and its police department after he was falsely arrested following an incorrect facial recognition match. Nijeer Parks spent 10 days in jail last year, including a week in "functional solitary confinement," following a shoplifting incident that January. After officers were called to a Hampton Inn in Woodbridge, the alleged shoplifter presented them with a Tennessee driver's license, which they determined was fake. When they attempted to arrest him after spotting what appeared to be a bag of marijuana in his pocket, the man fled in his rental car. One officer said he had to leap out of the way or he would have been hit.

A New Jersey man is suing local authorities who he says wrongly arrested him based on a false facial-recognition match, in a case that risks further inflaming debate over the utility and accuracy of the fast-emerging technology. The man, 33-year-old Nijeer Parks, spent more than a week in jail after police detained him in February 2019 on charges of shoplifting, assault and drug possession related to a Jan. 26 incident that year at a Hampton Inn hotel in Woodbridge, N.J., according to a complaint filed in New Jersey Superior Court. The criminal case was dismissed in November 2019, according to a court official in Middlesex County, where Woodbridge is located. Mr. Parks is now seeking unspecified damages, according to the complaint, over allegations including false arrest, civil-rights violations and emotional distress. The New Jersey state police, Woodbridge police and prosecutors in Middlesex County didn't immediately respond to requests for comment.

Facial recognition technology is known to have flaws. In 2019, a national study of over 100 facial recognition algorithms found that they did not work as well on Black and Asian faces. Two other Black men -- Robert Williams and Michael Oliver, both of whom live in the Detroit, Mich., area -- were also arrested for crimes they did not commit based on bad facial recognition matches. Like Mr. Parks, Mr. Oliver filed a lawsuit against the city over the wrongful arrest. Nathan Freed Wessler, an attorney with the American Civil Liberties Union who believes that police should stop using face recognition technology, said the three cases demonstrate "how this technology disproportionately harms the Black community."

Anyone who's ever been on an earnings call knows company executives already tend to look at the world through rose-colored glasses, but a new study by economics and machine learning researchers says that's getting worse, thanks to machine learning. The analysis found that companies are adapting their language in forecasts, SEC regulatory filings, and earnings calls due to the proliferation of AI used to analyze and derive signals from the words they use. In other words: Businesses are beginning to change the way they talk because they know machines are listening. Forms of natural language processing are used to parse and process text in the financial documents companies are required to submit to the SEC. Machine learning tools are then able to do things like summarize text or determine whether language used is positive, neutral, or negative.

We study the interplay of fairness, welfare, and equity considerations Studying the case of personalized pricing is conceptually challenging in personalized pricing based on customer features. Sellers because prices are a shared tool in drastically different are increasingly able to conduct price personalization based on domains: we consider lending/insurance, consumer goods, and public predictive modeling of demand conditional on covariates: setting provision. A crucial distinction is between value-based pricing customized interest rates, targeted discounts of consumer goods, that offers different prices to customers based on their estimated and personalized subsidies of scarce resources with positive externalities willingness to pay, and risk-based pricing which offers different like vaccines and bed nets. These different application areas prices to customers based on their estimated costs, as in lending may lead to different concerns around fairness, welfare, and equity and insurance [34]. While discrimination law is strongest in insurance on different objectives: price burdens on consumers, price envy, and lending, in lending, discrimination concerns often firm revenue, access to a good, equal access, and distributional consequences arise from individual agents providing offers from an actuariallyfair when the good in question further impacts downstream securitized rate sheet [9]. In particular, distributional concerns outcomes of interest. We conduct a comprehensive literature review regarding price optimization reflect overall concern for differentially in order to disentangle these different normative considerations adept/prepared/educated negotiating customers in insurance and propose a taxonomy of different objectives with mathematical and lending, but slight optimism in value-based pricing since lowincome definitions. We focus on observational metrics that do not assume individuals may be more price-sensitive [9]. Hence, the access to an underlying valuation distribution which is either unobserved majority of our analysis will focus on value-based pricing, which due to binary feedback or ill-defined due to overriding lends itself more readily to price optimization.

Artificial Intelligence (AI) is not just a buzzword, but a crucial part of the technology landscape. AI is changing every industry and business function, which results in increased interest in its applications, subdomains and related fields. This makes AI companies the top leaders driving the technology swift. AI helps us to optimise and automate crucial business processes, gather essential data and transform the world, one step at a time. From Google and Amazon to Apple and Microsoft, every major tech company is dedicating resources to breakthroughs in artificial intelligence. As big enterprises are busy acquiring or merging with other emerging inventions, small AI companies are also working hard to develop their own intelligent technology and services. By leveraging artificial intelligence, organizations get an innovative edge in the digital age. AI consults are also working to provide companies with expertise that can help them grow. In this digital era, AI is also a significant place for investment. AI companies are constantly developing the latest products to provide the simplest solutions. Henceforth, Analytics Insight brings you the list of top 100 AI companies that are leading the technology drive towards a better tomorrow. AEye develops advanced vision hardware, software, and algorithms that act as the eyes and visual cortex of autonomous vehicles. AEye is an artificial perception pioneer and creator of iDAR, a new form of intelligent data collection that acts as the eyes and visual cortex of autonomous vehicles. Since its demonstration of its solid state LiDAR scanner in 2013, AEye has pioneered breakthroughs in intelligent sensing. Their mission was to acquire the most information with the fewest ones and zeros. This would allow AEye to drive the automotive industry into the next realm of autonomy. Algorithmia invented the AI Layer.

Google this year moved to tighten control over its scientists' papers by launching a "sensitive topics" review, and in at least three cases requested authors refrain from casting its technology in a negative light, according to internal communications and interviews with researchers involved in the work. Google's new review procedure asks that researchers consult legal, policy and public relations teams before pursuing topics such as face and sentiment analysis and categorizations of race, gender or political affiliation, according to internal webpages explaining the policy. "Advances in technology and the growing complexity of our external environment are increasingly leading to situations where seemingly inoffensive projects raise ethical, reputational, regulatory or legal issues," one of the pages for research staff stated. Reuters could not determine the date of the post, though three current employees said the policy began in June. Google declined to comment for this story.

The history of invention is a history of knowledge spillovers. There is persistent evidence of knowledge flowing from one firm, industry, sector or region to another, either by accident or by design, enabling other inventions to be developed.1,6,9,13 For example, Thomas Edison's invention of the "electronic indicator" (US patent 307,031: 1884) spurred the development by John Fleming and Lee De Forest in early 20th century of early vacuum tubes which eventually enabled not just long-distance telecommunication but also early computers (for example, Guarnier10). Edison, in turn, learned from his contemporaries including Frederick Guthrie.11 It appears that little of this mutual learning and knowledge exchange was paid for and can thus be called a "spillover," that is, an unintended flow of valuable knowledge, an example of a positive externality. Information technologies have been a major source of knowledge spillovers.a Information is a basic ingredient of invention, and technologies that facilitate the manipulation and communication of information should also facilitate invention.

There is an increasing interest in the entwining of the field of antitrust with the field of Artificial Intelligence (AI), frequently referred to jointly as Antitrust and AI (AAI) in the research literature. This study focuses on the synergies entangling antitrust and AI, doing so to extend the literature by proffering the primary ways that these two fields intersect, consisting of: (1) the application of antitrust to AI, and (2) the application of AI to antitrust. To date, most of the existing research on this intermixing has concentrated on the former, namely the application of antitrust to AI, entailing how the marketplace will be altered by the advent of AI and the potential for adverse antitrust behaviors arising accordingly. Opting to explore more deeply the other side of this coin, this research closely examines the application of AI to antitrust and establishes an antitrust vigilance lifecycle to which AI is predicted to be substantively infused for purposes of enabling and bolstering antitrust detection, enforcement, and post-enforcement monitoring. Furthermore, a gradual and incremental injection of AI into antitrust vigilance is anticipated to occur as significant advances emerge amidst the Levels of Autonomy (LoA) for AI Legal Reasoning (AILR).

We participated in the two case law tasks, i.e., the legal case retrieval task and the legal case entailment task. Task 1 (the retrieval task) aims to automatically identify supporting cases from the case law corpus given a new case, and Task 2 (the entailment task) to identify specific paragraphs that entail the decision of a new case in a relevant case. In both tasks, we employed the neural models for semantic understanding and the traditional retrieval models for exact matching. As a result, our team ("TLIR") ranked 2nd among all of the teams in Task 1 and 3rd among teams in Task 2. Experimental results suggest that combing models of semantic understanding and exact matching benefits the legal case retrieval task while the legal case entailment task relies more on semantic understanding.