Artificial intelligence (AI) may provide great benefits for society but must be overseen rigorously to protect Canadians' privacy, the federal privacy watchdog says. The Office of the Privacy Commissioner of Canada said AI uses are based on individuals' personal information and can have serious consequences for privacy as AI models have the capability to analyze, infer and predict aspects of behaviour and interests. "Artificial intelligence has immense promise, but it must be implemented in ways that respect privacy, equality and other human rights," said Commissioner Daniel Therrien. "A rights-based approach will support innovation and the responsible development of artificial intelligence." A problem with the growing use of AI, though, explained McGill University's faculty of law professor Ignacio Cofone, is that people cannot opt out of data collection.

Solutions to fix algorithmic bias could collide with law. Two scholars propose a solution. Proposed solutions to fix algorithmic bias could conflict with Supreme Court rulings on equal protection, legal scholars note. As AI and predictive algorithms permeate ever more areas of decision making, from setting bail to evaluating job applications to making home loans, what happens when an algorithm arbitrarily discriminates against women, African-Americans, or other groups? It happens all the time.

In September 2019, four researchers wrote to the publisher Wiley to "respectfully ask" that it immediately retract a scientific paper. The study, published in 2018, had trained algorithms to distinguish faces of Uyghur people, a predominantly Muslim minority ethnic group in China, from those of Korean and Tibetan ethnicity1. China had already been internationally condemned for its heavy surveillance and mass detentions of Uyghurs in camps in the northwestern province of Xinjiang -- which the government says are re-education centres aimed at quelling a terrorist movement. According to media reports, authorities in Xinjiang have used surveillance cameras equipped with software attuned to Uyghur faces. As a result, many researchers found it disturbing that academics had tried to build such algorithms -- and that a US journal had published a research paper on the topic. And the 2018 study wasn't the only one: journals from publishers including Springer Nature, Elsevier and the Institute of Electrical and Electronics Engineers (IEEE) had also published peer-reviewed papers that describe using facial recognition to identify Uyghurs and members of other Chinese minority groups. The complaint, which launched an ongoing investigation, was one foray in a growing push by some scientists and human-rights activists to get the scientific community to take a firmer stance against unethical facial-recognition research.

Cense.ai is an Artificial Intelligence company that works in a wide range of areas. According to the company website, Cense.ai It is this last practice that led to the company exposing over 2.5 million medical records. According to researcher Jeremiah Fowler, all of the records were readily available to view or download by anyone with an Internet connection. Though it remains unclear how long the data was available online, Fowler made the discovery on July 7th, 2020.

Automation journeys are evolving with AI ML capabilities and better data management techniques. The automation of processes has advantages in many areas of business. Helping to create predictable success, like the autopilot technology on a plane has been perfect over many years and by decreasing the time that it takes to complete manual processes and removing the chance of human error. Automating processes also have the benefit of streamlining workflows. The largest gains can be achieved by automating very large or time-consuming processes.

This article is part of the Free Speech Project, a collaboration between Future Tense and the Tech, Law, & Security Program at American University Washington College of Law that examines the ways technology is influencing how we think about speech. What could be one of the most consequential First Amendment cases of the digital age is pending before a court in Illinois and will likely be argued before the end of the year. The case concerns Clearview AI, the technology company that surreptitiously scraped 3 billion images from the internet to feed a facial recognition app it sold to law enforcement agencies. Now confronting multiple lawsuits based on an Illinois privacy law, the company has retained Floyd Abrams, the prominent First Amendment litigator, to argue that its business activities are constitutionally protected. Landing Abrams was a coup for Clearview, but whether anyone else should be celebrating is less clear.

The COVID-19 pandemic has elevated the role of technology in the workplace, and more employers are relying on artificial intelligence, machine learning and virtual reality to save money and limit in-person contact. These technologies can be effective tools for hiring, training and assessing employee performance, as well as creating meaningful interactions during a time of isolation. However, employers must ensure that their use of technology doesn't run afoul of employment and labor laws. "It's incredibly important for HR organizations and hiring managers to understand the nuances of the technology that they're using if it is making decisions on their behalf," said Marc Goldberg, chief technology officer at the Society for Human Resource Management (SHRM) in Alexandria, Va. He was speaking during a panel discussion at the American Bar Association's 14th Annual Labor and Employment Law Conference, which was held virtually.

Nitrogen dioxide (NO2) is one of the most important atmospheric pollutants. However, current ground-level NO2 concentration data are lack of either high-resolution coverage or full coverage national wide, due to the poor quality of source data and the computing power of the models. To our knowledge, this study is the first to estimate the ground-level NO2 concentration in China with national coverage as well as relatively high spatiotemporal resolution (0.25 degree; daily intervals) over the newest past 6 years (2013-2018). We advanced a Random Forest model integrated K-means (RF-K) for the estimates with multi-source parameters. Besides meteorological parameters, satellite retrievals parameters, we also, for the first time, introduce socio-economic parameters to assess the impact by human activities. The results show that: (1) the RF-K model we developed shows better prediction performance than other models, with cross-validation R2 = 0.64 (MAPE = 34.78%). (2) The annual average concentration of NO2 in China showed a weak increasing trend . While in the economic zones such as Beijing-Tianjin-Hebei region, Yangtze River Delta, and Pearl River Delta, the NO2 concentration there even decreased or remained unchanged, especially in spring. Our dataset has verified that pollutant controlling targets have been achieved in these areas. With mapping daily nationwide ground-level NO2 concentrations, this study provides timely data with high quality for air quality management for China. We provide a universal model framework to quickly generate a timely national atmospheric pollutants concentration map with a high spatial-temporal resolution, based on improved machine learning methods.

We consider settings in which the data of interest correspond to pairs of ordered times, e.g, the birth times of the first and second child, the times at which a new user creates an account and makes the first purchase on a website, and the entry and survival times of patients in a clinical trial. In these settings, the two times are not independent (the second occurs after the first), yet it is still of interest to determine whether there exists significant dependence {\em beyond} their ordering in time. We refer to this notion as "quasi-(in)dependence". For instance, in a clinical trial, to avoid biased selection, we might wish to verify that recruitment times are quasi-independent of survival times, where dependencies might arise due to seasonal effects. In this paper, we propose a nonparametric statistical test of quasi-independence. Our test considers a potentially infinite space of alternatives, making it suitable for complex data where the nature of the possible quasi-dependence is not known in advance. Standard parametric approaches are recovered as special cases, such as the classical conditional Kendall's tau, and log-rank tests. The tests apply in the right-censored setting: an essential feature in clinical trials, where patients can withdraw from the study. We provide an asymptotic analysis of our test-statistic, and demonstrate in experiments that our test obtains better power than existing approaches, while being more computationally efficient.

The weather is hot, the trees brimming with life … " So begins the baritone voice-over in a video shot in the summer of 2018 by the Chinese telecommunications giant Huawei and posted to YouTube. It chronicles a corporate event in the slightly corny style of a 1960s educational film, starting with aerial drone footage of Huawei's campus--an island of lush greenery surrounded by the high-rise buildings of the city known as China's Silicon Valley. A spirited orchestral version of Beethoven's "Turkish March" plays as a town car wends its way through the campus, pulling up to a stately white structure mixing classical Greek architecture and the wide overhanging rooftops of China's great pagodas. There's a bit of the White House tossed in too. This feature appears in the December 2020/January 2021 issue.