The Estonian Ministry of Justice has officially asked Ott Velsberg, the country's chief data officer, to design a "robot judge" to take care of a backlog of small claims court disputes, Wired reports. The artificial intelligence-powered "judge" is supposed to analyze legal documents and other relevant information and come to a decision. Though a human judge will have an opportunity to revise those decisions, the project is a striking example of justice by artificial intelligence. Estonia, a tiny Northern European nation of fewer than 1.4 million inhabitants, has made impressive strides in digitizing, streamlining, and modernizing its government functions. Estonia famously launched its "e-residency" program that allows practically anybody -- including foreigners -- to access Estonian government services.

Federated learning (FL) learns a model jointly from a set of participating devices without sharing each other's privately held data. The characteristics of non-iid data across the network, low device participation, and the mandate that data remain private bring challenges in understanding the convergence of FL algorithms, particularly in regards to how convergence scales with the number of participating devices. In this paper, we focus on Federated Averaging (FedAvg)--the most widely used and effective FL algorithm in use today--and provide a comprehensive study of its convergence rate. Although FedAvg has recently been studied by an emerging line of literature, it remains open as to how FedAvg's convergence scales with the number of participating devices in the FL setting--a crucial question whose answer would shed light on the performance of FedAvg in large FL systems. We fill this gap by establishing convergence guarantees for FedAvg under three classes of problems: strongly convex smooth, convex smooth, and overparameterized strongly convex smooth problems. We show that FedAvg enjoys linear speedup in each case, although with different convergence rates. For each class, we also characterize the corresponding convergence rates for the Nesterov accelerated FedAvg algorithm in the FL setting: to the best of our knowledge, these are the first linear speedup guarantees for FedAvg when Nesterov acceleration is used. To accelerate FedAvg, we also design a new momentum-based FL algorithm that further improves the convergence rate in overparameterized linear regression problems. Empirical studies of the algorithms in various settings have supported our theoretical results.

A false facial recognition match has led to the arrest of another innocent person. According to the Detroit Free Press, police in the city arrested a man for allegedly reaching into a person's car, taking their phone and throwing it, breaking the case and damaging the screen in the process. Facial recognition flagged Michael Oliver as a possible suspect, and the victim identified him in a photo lineup as the person who damaged their phone. Oliver was charged with a felony count of larceny over the May 2019 incident. He said he didn't commit the crime and the evidence supported his claim.

The high-profile case of a Black man wrongly arrested this year wasn't the first misidentification linked to controversial facial recognition technology used by Detroit police, the Free Press has learned. Last year, a 25-year-old Detroit man was wrongly accused of a felony for supposedly reaching into a teacher's vehicle, grabbing a cellphone and throwing it, cracking the screen and breaking the case. Detroit police used facial recognition technology in that investigation, too. It identified Michael Oliver as an investigative lead. After that hit, the teacher whose phone was snatched from his hands identified Oliver in a photo lineup as the person responsible.

Growing technologies such as artificial intelligence have incredible potential. However, they also can come with ethical concerns, such as privacy violations and data safety. These issues must be addressed before people can safely implement emerging technologies in their daily lives. As industry leaders, the members of Forbes Technology Council keep a close eye on issues impacting the field. Below, they share 15 ethical crises they're concerned about and what can be done to remedy them.

The high-profile case of a Black man wrongly arrested earlier this year wasn't the first misidentification linked to controversial facial recognition technology used by Detroit police, the Free Press has learned. Last year, a 25-year-old Detroit man was wrongly accused of a felony for supposedly reaching into a teacher's vehicle, grabbing a cell phone and throwing it, cracking the screen and breaking the case. Detroit police used facial recognition technology in that investigation, too. It identified Michael Oliver as an investigative lead. After that hit, the teacher who had his phone snatched from his hands identified Oliver in a photo lineup as the person responsible.

According to several studies by Intel spanning 2018, 2019, and 2020, AI and edge computing make it possible to positively identify up to 99% of visible manufacturing defects before a product ever leaves the line. "One of the most important things manufacturers care about is product quality," says Brian McCarson, Vice President and Senior Principal Engineer, Internet of Things Group (IOTG) at Intel Corporation and a featured speaker at Transform, VentureBeat's upcoming digital conference. "Manufactures prefer throwing away fewer defective products. They strive to have less rework and fewer customer returns. They also want to reduce the cost of their operations by making their tools and processes more efficient, and improve the reliability of their machines so they can proactively do maintenance before it is too late and have more predictable uptime."

The American Civil Liberties Union (ACLU) has filed a formal complaint against Detroit police over what it says is the first known example of a wrongful arrest caused by faulty facial recognition technology. Robert Julian-Borchak Williams, an African American man, was arrested after a facial recognition system falsely matched his photo with security footage of a shoplifter. The New York Times reports that the ACLU is calling for the dismissal of Williams' case and for his information to be removed from Detroit's criminal databases, and prosecutors have since agreed to delete his data. Facial recognition technology has been criticized for years, with researchers showing it to be biased against members of different races and ethnicities. But its use by law enforcement has grown even more controversial in recent weeks following nationwide protests against police brutality and racism.

Phoebe V. Moore Protecting Workers in the Digital Age: Technology, Outsourcing, and the Growing Precariousness of Work Janine Berg Artificial Intelligence is Watching You at Work: Digital Surveillance, Employee Monitoring, and Regulatory Issues in the EU Context Antonio Aloisi & Elena Gramano What if Your Boss was an Algorithm? Economic Incentives, Legal Challenges, and the Rise of Artificial Intelligence at Work Jeremias Adams-Prassl Privacy 4.0 at Work: Regulating Employment, Technology, and Automation Frank Hendrickx A Seat at the Table: Negotiating Data Processing in the Workplace Ilaria Armaroli & Emanuele Dagnino Job Automation in the 1960s: A Discourse Ahead of its Time (And for Our Time) Miriam A. Cherry

In facial recognition and AI development, computers are trained on massive sets of data, millions of pictures gathered from all over the web. There are only a few publicly available datasets, and a lot of organizations use them. He and Abeba Birhane, at University College Dublin, published a paper recently examining these academic datasets. Most of the pictures are gathered without consent, people can be identified in them and there are racist and pornographic images and text. And even the idea of labeling someone a lawyer or a woman or a criminal based on appearance?