The proliferation of personalized recommendation technologies has raised concerns about discrepancies in their recommendation performance across different genders, age groups, and racial or ethnic populations. This varying degree of performance could impact users’ trust in the system and may pose legal and ethical issues in domains where fairness and equity are critical concerns, like job recommendation. In this paper, we investigate several potential factors that could be associated with discriminatory performance of a recommendation algorithm for women versus men. We specifically study several characteristics of user profiles and analyze their possible associations with disparate behavior of the system towards different genders. These characteristics include the anomaly in rating behavior, the entropy of users’ profiles, and the users’ profile size. Our experimental results on a public dataset using four recommendation algorithms show that, based on all the three mentioned factors, women get less accurate recommendations than men indicating an unfair nature of recommendation algorithms across genders.

A teenager standing up for his right to play hours of video games has launched a crowdfunding campaign for a lawsuit to challenge guidelines in Shikoku that limit children's gaming time. The 17-year-old, who asked to be identified only by his first name Wataru, has enlisted the support of his mother and a lawyer for the first-of-its-kind ordinance, which was issued by Kagawa Prefecture. The ordinance calls for children to be limited to an hour a day of gaming during the week, and 90 minutes during school holidays. It also suggests that children 12 to 15 not be allowed to use smartphones later than 9 p.m., with the deadline pushed back to 10 p.m. for those between 15 and 18. But while the rules are just guidelines with no enforcement mechanism, Wataru said he was inspired to challenge them on principle. "How long children are allowed to play games or use a smartphone should be rules set by each family, not by the government," he said.

Artificial Intelligence is developing fast. It will change our lives by improving healthcare (e.g.

Watch Benjamin Alarie, co-founder and CEO of Blue J Legal, discuss the expanding role of artificial intelligence in tax with contributing editor at Tax Notes Federal Benjamin Willis. Benjamin Alarie: When we talk about machine learning and artificial intelligence of the law, what we're doing is talking about collecting the raw materials, the rulings, the cases, the legislation, the regs, all that information, and bringing it to bear on a particular problem. We're synthesizing all of those materials to make a prediction about how a new situation would likely be decided by the courts. We have lots of data out there in the form of rulings, in the form of judgments that we can collect as good examples of how the courts have decided these matters in the past. And we can reverse engineer using machine learning methods how the courts are mapping the facts of different situations into outcomes.

Identifying the optimal level of taxation is quite complex. Human behaviour is highly unpredictable and gathering data can be time consuming. Despite decades of economic research being put into finding the optimal tax rate, it remains an open problem. But, scientists at the US business technology company, Salesforce, believe they may have found the key to solving the problem – Artificial Intelligence. The team has developed an AI system called the AI Economist, which uses reinforcement learning technology to identify the optimal level of taxation to make reduce inequality.

Having helped incorporate Paradox less than four years ago, we were proud to represent Paradox in this important financing, which signifies its status as one of Arizona's premier emerging growth companies,

The United States Patent and Trademark Office officially selected a new partner to support its increasing adoption of artificial intelligence and machine learning capabilities. General Dynamics Information Technology on Monday announced it was awarded a contract worth up to $50 million through USPTO's Intelligent Automation and Innovation Support Services blanket purchase agreement. GDIT is the latest of more than a dozen companies the agency tapped under the future-facing BPA. Other businesses who've made their own recent announcements detailing partnerships via the agreement include Octo and Steampunk. In the announcement, Vice President & General Manager Christopher Hegedus for GDIT's Diplomacy, Commerce and Government Operations business area noted the company's supported the agency for nearly two decades, and through this "new work, [aims to bring its] AI, ML and robotic process automation expertise to help USPTO develop solutions that accelerate the patent and trademark process to benefit American innovators."

The mix of data, technology, and talent has made it feasible for the present smart systems to arrive at a basic point that drives exceptional development in AI investment. Funding for AI and machine learning startups has been developing at a yearly development pace of almost 60% since 2010 and the organizations are moving past a significant stretch of exploratory AI into a period of exponential AI. As specialists state, we are entering a "Race Against the Machine," and a "Fourth Industrial Revolution." As per Crunchbase, there are 8,705 startups and organizations today depending on AI and machine learning for their essential applications, products, and services. Practically 83% of AI and machine learning startups that Crunchbase tracks, had just three or fewer funding rounds, the most well-known being seed rounds, angel rounds, and early-stage rounds.

Quality control is an essential process in manufacturing to make the product defect-free as well as to meet customer needs. The automation of this process is important to maintain high quality along with the high manufacturing throughput. With recent developments in deep learning and computer vision technologies, it has become possible to detect various features from the images with near-human accuracy. However, many of these approaches are data intensive. Training and deployment of such a system on manufacturing floors may become expensive and time-consuming. The need for large amounts of training data is one of the limitations of the applicability of these approaches in real-world manufacturing systems. In this work, we propose the application of a Siamese convolutional neural network to do one-shot recognition for such a task. Our results demonstrate how one-shot learning can be used in quality control of steel by identification of defects on the steel surface. This method can significantly reduce the requirements of training data and can also be run in real-time.

This article identifies a critical incompatibility between European notions of discrimination and existing statistical measures of fairness. First, we review the evidential requirements to bring a claim under EU non-discrimination law. Due to the disparate nature of algorithmic and human discrimination, the EU's current requirements are too contextual, reliant on intuition, and open to judicial interpretation to be automated. Second, we show how the legal protection offered by non-discrimination law is challenged when AI, not humans, discriminate. Humans discriminate due to negative attitudes (e.g. stereotypes, prejudice) and unintentional biases (e.g. organisational practices or internalised stereotypes) which can act as a signal to victims that discrimination has occurred. Finally, we examine how existing work on fairness in machine learning lines up with procedures for assessing cases under EU non-discrimination law. We propose "conditional demographic disparity" (CDD) as a standard baseline statistical measurement that aligns with the European Court of Justice's "gold standard." Establishing a standard set of statistical evidence for automated discrimination cases can help ensure consistent procedures for assessment, but not judicial interpretation, of cases involving AI and automated systems. Through this proposal for procedural regularity in the identification and assessment of automated discrimination, we clarify how to build considerations of fairness into automated systems as far as possible while still respecting and enabling the contextual approach to judicial interpretation practiced under EU non-discrimination law. N.B. Abridged abstract