Google has been accused of a conspiracy and a cover-up over a disappearing image of Winston Churchill – but the affair appears to have been both more complicated and innocent than it first appeared. Outcry was prompted among some specific people on social media over the weekend when it emerged that searching for Winston Churchill no longer showed an image of the former prime minister, and instead just text responses to the query. The search company was attacked by people including culture secretary Oliver Dowden, who expressed his "concern" that the image had been removed for sinister reasons. It disappeared amid ongoing debate about the place of statues in public life, racial inequality, and Churchill's legacy, leading some to suggest the decision was a political move. But Google said it was in fact the result of a bug that occurred when Google tried to change rather than remove the image.

Mozilla and the Electronic Freedom Foundation (EFF) have presented an open letter to Zoom after it said it would require customers to pay for end-to-end encryption. The letter, signed by over 19,000 internet users, says that "best-in-class security should not be something that only the wealthy or businesses can afford." The video conferencing software saw use boom during the coronavirus pandemic, as did other video calling applications such as Microsoft Teams and Houseparty. However, comments from its CEO Eric Yuan that the company would not encrypt conversations for free users so it can work better with law enforcement raised concerns for user security. The company had also shut down the account of a Tiananmen Square activist, who had a paid account, at the behest of the Chinese government.

The racial profiling and police brutality in the George Floyd incident and #BlackLivesMatter protests and rioting unfolded debate on many levels. One of them is flaws in Artificial Intelligence that end up creating a racial bias in technology which is deemed as an instrumental force to bring digital age. However, all hope is not lost, especially in the Australian start-up sector. Presenting, Akin and Unleash Live, AI-backed companies founded by Liesl Yearsley and Hanno Blankenstein respectively. While Akin, uses AI to build bots that can converse with humans in a lifelike way, Unleash Live employs AI for real-time analysis of video footage coming from security cameras and drones.

Artificial Intelligence and Machine Learning are becoming increasingly present in several aspects of human life, especially, those dealing with decision making. Many of these algorithmic decisions are taken without human supervision and through decision making processes that are not transparent. This raises concerns regarding the potential bias of these processes towards certain groups of society, which may entail unfair results and, possibly, violations of human rights. Dealing with such biased models is one of the major concerns to maintain the public trust. In this paper, we address the question of process or procedural fairness. More precisely, we consider the problem of making classifiers fairer by reducing their dependence on sensitive features while increasing (or, at least, maintaining) their accuracy. To achieve both, we draw inspiration from "dropout" techniques in neural based approaches, and propose a framework that relies on "feature drop-out" to tackle process fairness. We make use of "LIME Explanations" to assess a classifier's fairness and to determine the sensitive features to remove. This produces a pool of classifiers (through feature dropout) whose ensemble is shown empirically to be less dependent on sensitive features, and with improved or no impact on accuracy.

When science fiction writer Isaac Asimov introduced the Three Laws of Robotics to the world in 1942, practical robotic applications such as industrial pneumatic arms, all-transistor calculators and even the term "artificial intelligence" itself were all still a decade or two in the future. Asimov's laws boil down to three simple maxims: protect humans; obey humans; if it doesn't violate rule one or two, protect itself. Seems simple and sensible enough, yet the limits and internal tensions of these basic laws have inspired writers to dream up a wide range of science fiction dystopias, from 2001 to Blade Runner to the Terminator. And let's not forget to add Asimov's own collection of stories, I, Robot, which features the Three Laws, to the list. For business leaders, ushering in an AI-driven global calamity isn't a top-of-mind concern, but even avoiding smaller risks can be a major challenge.

In the race to enable manufacturing plants to increase production in the face of an intermittent human workforce, manufacturers are looking at how to supplement their cameras with AI to give human inspectors the ability to spot defective products immediately and correct the problem. While machine vision has been around for more than 60 years, the recent surge in the popularity of deep learning has elevated this sometimes misunderstood technology to the attention of major manufacturers globally. As CEO of a deep learning software company, I've seen how deep learning is a natural next step from machine vision, and has the potential to drive innovation for manufacturers. How does deep learning differ from machine vision, and how can manufacturers leverage this natural evolution of camera technology to cope with real-world demands? In the 1960s, several groups of scientists, many of them in the Boston area, set forth to solve "the machine vision problem."

Some instances of racism in gaming are glaring and brazen, such as players creating usernames, or online aliases, using the n-word. In the week after Floyd's death, a video posted on Reddit showed a user scrolling through a series of explicitly racist usernames on "Call of Duty: Modern Warfare," Activision's latest title in one of the best selling video games franchises of all time. In response to this and other similar posts, Infinity Ward, the Activision-owned studio that made the game, tweeted that they "need to do a better job" and are issuing thousands of bans daily. The company said it will add a new in-game reporting system, "additional resources to monitor and ID racist content," and other features, including more permanent bans. The game was released in October of last year.

They met for a few drinks at a bar before going to his apartment in Layton, authorities have said. He called 911 to report the slaying early Sunday morning and told police to shoot him, according to court documents. Hunsaker told police he choked and then stabbed her unprovoked as they cuddled, according to the document.

Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships between features into consideration. Unfortunately, in practice, the true underlying structural causal model is generally unknown. In this work, we first show that it is impossible to guarantee recourse without access to the true structural equations. To address this limitation, we propose two probabilistic approaches to select optimal actions that achieve recourse with high probability given limited causal knowledge (e.g., only the causal graph). The first captures uncertainty over structural equations under additive Gaussian noise, and uses Bayesian model averaging to estimate the counterfactual distribution. The second removes any assumptions on the structural equations by instead computing the average effect of recourse actions on individuals similar to the person who seeks recourse, leading to a novel subpopulation-based interventional notion of recourse. We then derive a gradient-based procedure for selecting optimal recourse actions, and empirically show that the proposed approaches lead to more reliable recommendations under imperfect causal knowledge than non-probabilistic baselines.

Tech giants have struggled for years to convince the public that they are committed to diversifying their own massive workforces, but the demographics have only slowly changed in the past decade. Google's workforce is 54.4 percent white and 3.3 percent black, according to its 2019 diversity report. Apple's U.S. workforce is 50 percent white and 9 percent black. Amazon's report shows a more diverse makeup -- its U.S. workforce is 34.7 percent white and 26.5 percent black -- though its statistics include low-paying warehouse jobs as well as more lucrative white collar positions.