Algorithms play a growing role in our lives, even as their flaws are becoming more apparent: A Michigan man wrongly accused of fraud had to file for bankruptcy; automated screening tools disproportionately harm people of color who want to buy a home or rent an apartment; Black Facebook users were subjected to more abuse than white users. Other automated systems have improperly rated teachers, graded students, and flagged people with dark skin more often for cheating on tests. Now, efforts are underway to better understand how AI works and hold users accountable. New York's City Council last month adopted a law requiring audits of algorithms used by employers in hiring or promotion. The law, the first of its kind in the nation, requires employers to bring in outsiders to assess whether an algorithm exhibits bias based on sex, race, or ethnicity.

The company that owns Tinder will pay $441 million to the popular dating app's founders to settle a dispute over the valuation of stock options, documents showed Wednesday. The suit filed in New York in 2018 contended that Tinder owner Match Group, and its then parent firm InterActiveCorp, schemed to dramatically drive down the value of stock options and then eliminate them altogether. Co-creators Sean Rad, Justin Mateen and Jonathan Badeen alleged Match and IAC relied on bogus figures to arrive at a valuation of $3 billion in 2017 -- when Tinder was actually worth more than four times that. Tinder's owner is paying the app's founders millions to settle a lawsuit Photo: AFP / Aamir QURESHI Created in 2012, Tinder now has more than 10 million paying users who can quickly scroll through possible romantic matches, and then swipe left or right to signal interest. With options on about 20 percent of Tinder's stock, the founders and their early employees felt they had been shortchanged by several billion dollars.

Dr. Stephen Thaler is the owner of an AI machine that he calls the "Device for the Autonomous Bootstrapping of Unified Sentience" or "DABUS" for short.5 In 2019, Dr. Thaler filed U.S. patent applications claiming "a light beacon that flashes in a new and inventive manner to attract attention," which he called a "Neural Flame," and a "beverage container based on fractal geometry," called a "Fractal Container."6 In Dr. Thaler's telling, DABUS is "a type of'creativity machine,' " which "conceived" the Neural Flame and the Fractal Container. As such, Dr. Thaler argues that DABUS should be considered "the sole inventor" of the Neural Flame and the Fractal Container.7 According to Dr. Thaler, he spent a decade building on his earlier work to develop DABUS, which he calls "a totally new AI paradigm," one that can "appreciate [its] creations"8 and produce a "human readable" form of "pidgin language."9

Modeling law search and retrieval as prediction problems has recently emerged as a predominant approach in law intelligence. Focusing on the law article retrieval task, we present a deep learning framework named LamBERTa, which is designed for civil-law codes, and specifically trained on the Italian civil code. To our knowledge, this is the first study proposing an advanced approach to law article prediction for the Italian legal system based on a BERT (Bidirectional Encoder Representations from Transformers) learning framework, which has recently attracted increased attention among deep learning approaches, showing outstanding effectiveness in several natural language processing and learning tasks. We define LamBERTa models by fine-tuning an Italian pre-trained BERT on the Italian civil code or its portions, for law article retrieval as a classification task. One key aspect of our LamBERTa framework is that we conceived it to address an extreme classification scenario, which is characterized by a high number of classes, the few-shot learning problem, and the lack of test query benchmarks for Italian legal prediction tasks. To solve such issues, we define different methods for the unsupervised labeling of the law articles, which can in principle be applied to any law article code system. We provide insights into the explainability and interpretability of our LamBERTa models, and we present an extensive experimental analysis over query sets of different type, for single-label as well as multi-label evaluation tasks. Empirical evidence has shown the effectiveness of LamBERTa, and also its superiority against widely used deep-learning text classifiers and a few-shot learner conceived for an attribute-aware prediction task.

Explainable AI (XAI) research has been booming, but the question "$\textbf{To whom}$ are we making AI explainable?" is yet to gain sufficient attention. Not much of XAI is comprehensible to non-AI experts, who nonetheless, are the primary audience and major stakeholders of deployed AI systems in practice. The gap is glaring: what is considered "explained" to AI-experts versus non-experts are very different in practical scenarios. Hence, this gap produced two distinct cultures of expectations, goals, and forms of XAI in real-life AI deployments. We advocate that it is critical to develop XAI methods for non-technical audiences. We then present a real-life case study, where AI experts provided non-technical explanations of AI decisions to non-technical stakeholders, and completed a successful deployment in a highly regulated industry. We then synthesize lessons learned from the case, and share a list of suggestions for AI experts to consider when explaining AI decisions to non-technical stakeholders.

So far, artificial intelligence (AI) is a new enough technology in the business world that it's mostly evaded the long arm of regulatory agencies and standards. But with mounting concerns over privacy and other sensitive areas, that grace period is about to end, according to predictions released on Wednesday by consulting firm Deloitte. Looking at the overall AI landscape, including machine learning, deep learning and neural networks, Deloitte said it believes that next year will pave the way for greater discussions about regulating these popular but sometimes problematic technologies. These discussions will trigger enforced regulations in 2023 and beyond, the firm said. Fears have arisen over AI in a few areas.

In August, Savannah's entire monthly income was at stake. OnlyFans, the social media platform where she built her career, making an average of $2,000 a month from subscribers, had just announced it would be removing content like hers from the site. But there was little she could do about it. She remembers thinking: "OK, well, this is another Thursday, I might as well finish my Chick-Fil-A, and I'm just gonna chill here and wait for us to get some sort of response." Savannah, 24, is part of a vibrant, supportive community of online sex workers that underwrite OnlyFans's considerable financial success; it's now valued at over $1 billion. But in a move that may foreshadow changes to come, that community was shaken when OnlyFans announced it would be banning explicit content on the site. "The sky falls on OnlyFans, like, every three or four months," Savannah says, wryly.

Patient receives the world's first fully 3D-printed prosthetic eye, Russia may press criminal charges in 2018 ISS pressure leak incident, UK competition regulator orders Meta to sell Giphy.

Governments need to curb the power of tech companies and increase the power of workers in order to safeguard people from unsafe uses of artificial intelligence, Google's former AI ethics chief Timnit Gebru told a hearing at the European Parliament on Tuesday. "When asked what regulations need to be in place," she said, "I say the number one thing that would safeguard us from unsafe uses of AI is curbing the power of the companies who develop it, and increasing the power of those who speak up against not only the harms of AI but the tech companies' practices." Gebru said one good example was the state of California's Silence No More Act, which makes it illegal for companies to prevent employees from speaking out about harassment or discrimination. "I think this needs to be universal. I think that companies need to have much stronger punishments for violating any of these laws, like the huge aggressive union-busting activities that we see by Amazon," she continued.

The phrase "artificial intelligence" can conjure up images of machines that are able to think, and act, just like humans, independent of any oversight from actual, flesh and blood people. Movies versions of AI tend to feature super-intelligent machines attempting to overthrow humanity and conquer the world. The reality is more prosaic, and tends to describe software that can solve problems, find patterns and, to a certain extent, "learn". This is particularly useful when huge amounts of data need to be sorted and understood, and AI is already being used in a host of scenarios, particularly in the private sector. Examples include chatbots able to conduct online correspondence; online shopping sites which learn how to predict what you might want to buy; and AI journalists writing sports and business articles (this story was, I can assure you, written by a human).