The German Parliament in Berlin is said to have approved a new law on autonomous driving in the country. According to a media report, the new regulation, which is currently awaiting the President's signature, will allow companies to start making money from autonomous driving services. The new regulation is aimed to spur future development in technology as well. Autonomous vehicles will require humans to oversee the operations. A trained technician will monitor the autonomous vehicle from a remotely located command centre, eliminating the need to have a person at the driver's seat.

Thanks to Valerio De Stefano, Reuben Binns, Jeremias Adams-Prassl, Barend van Leeuwen, Aislinn Kelly-Lyth, Lilian Edwards, Natali Helberger, Christopher Marsden, Sarah Chander, Corinne Cath-Speth for comments and/or discussion; substantive and editorial input by Ulrich Gasper; and the conveners and participants of several workshops including one convened by Margot Kaminski, one by Burkhard Schäfer, one part of the 2nd ELLIS Workshop in Human-Centric Machine Learning; one between Lund University and the Labour Law Community; and one between Oxford, KU Leuven and UCL. A CC-BY 4.0 license applies to this article after 3 calendar months from publication have elapsed.

We present a new dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning. Existing graph-text paired datasets typically contain small graphs and short text (1 or few sentences), thus limiting the capabilities of the models that can be learned on the data. Our new dataset WikiGraphs is collected by pairing each Wikipedia article from the established WikiText-103 benchmark (Merity et al., 2016) with a subgraph from the Freebase knowledge graph (Bollacker et al., 2008). This makes it easy to benchmark against other state-of-the-art text generative models that are capable of generating long paragraphs of coherent text. Both the graphs and the text data are of significantly larger scale compared to prior graph-text paired datasets. We present baseline graph neural network and transformer model results on our dataset for 3 tasks: graph -> text generation, graph -> text retrieval and text -> graph retrieval. We show that better conditioning on the graph provides gains in generation and retrieval quality but there is still large room for improvement.

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The new shipping industry is nearly here. Amazon Inc. just applied for a patent on a new package delivery system capable of shipping consumer goods from the primary delivery vehicle to your door via a mini-sized delivery vehicle robot that ferries shipments to final end-point destinations, according to a filing with the United States Patent and Trademark Office. But we could still get the flying drone deliveries we secretly crave. In the last decade, Amazon and other delivery services have investigated the possibility of employing new technologies to transport packages from warehouses to consumers. The proposals have ranged from driverless vans housing smaller robots to flying drones that ship directly through the air to customer airspace (and then a parachute drop of packages). Electronic delivery robots were assumed to be a cheaper alternative to hiring and supporting humans to perform hard labor.

Data-driven analysis and detection of abusive online content covers many different tasks, phenomena, contexts, and methodologies. This paper systematically reviews abusive language dataset creation and content in conjunction with an open website for cataloguing abusive language data. This collection of knowledge leads to a synthesis providing evidence-based recommendations for practitioners working with this complex and highly diverse data.

Responsible AI, Ethical AI, AI for social good -- I am sure you must have heard these terms at some point or the other, whether you are a Data Scientist or not. "The development of full artificial intelligence could spell the end of the human race." And there my journey of understanding this critical aspect of the AI foundation started. I used to wonder how to relate ethics with AI which is just a series of algorithms, when, in fact, we have not been able to apply ethical behavior among ourselves. As per the AI index report published by the Stanford University Institute for Human-Centered AI, cybersecurity and regulatory compliance are among the top risks identified by AI/ML-oriented organizations.

While the U.S. is figuring out privacy laws at the state and federal level, artificial and augmented intelligence (AI) is evolving and becoming commonplace for businesses and consumers. These technologies are driving new privacy concerns. Years ago, consumers feared a stolen Social Security number. Now, organizations can uncover political views, purchasing habits, and much more. The repercussions of data are broader and deeper than ever.

In March 2020, two months after The New York Times exposed that Clearview AI had scraped billions of images from the internet to create a facial recognition database, Thomas Smith received a dossier encompassing most of his digital life. Using the recently enacted California Consumer Privacy Act, Smith asked Clearview for what they had on him. The company sent him pictures that spanned moments throughout his adult life: a photo from when he got married and started a blog with his wife, another when he was profiled by his college's alumni magazine, even a profile photo from a Python coding meetup he had attended a few years ago. "That's what really threw me: All the things that I had posted to Facebook and figured, 'Nobody's going to ever look for that,' and here it is all laid out in a database," Smith told The Verge. Clearview's massive surveillance apparatus claims to hold 3 billion photos, accessible to any law enforcement agency with a subscription, and it's likely you or people you know have been scooped up in the company's dragnet.

Since graduating from a US university four years ago, Kevin Carballo has lost count of the number of times he has applied for a job only to receive a swift, automated rejection email - sometimes just hours after applying. Like many job seekers around the world, Carballo's applications are increasingly being screened by algorithms built to automatically flag attractive applicants to hiring managers. "There's no way to apply for a job these days without being analyzed by some sort of automated system," said Carballo, 27, who is Latino and the first member of his family to go to university. "It feels like shooting in the dark while being blindfolded - there's just no way for me to tell my full story when a machine is assessing me," Carballo, who hoped to get work experience at a law firm before applying to law school, told the Thomson Reuters Foundation by phone. From Artificial Intelligence (AI) programs that assess an applicant's facial expressions during a video interview, to resume screening platforms predicting job performance, the AI recruitment industry is valued at more than $500 million.