Google is facing a lawsuit surrounding allegations that it illegally tracked millions of people through their phones, even after they set their privacy settings to disable the sharing of their location history. Napoleon Patacsil of San Diego is seeking class-action status on behalf of US users of Android phones and Apple iPhones who were tracked against their will and knowledge. Mr Patacsil claims Google's "principal goal" was to "surreptitiously monitor" phone users and let third parties do the same. "Google represented that a user'can turn off Location History at any time. With Location History off, the places you go are no longer stored.' This simply was not true," the complaint states.

Opponents of the Federal Communications Commission have outlined their chief arguments on net neutrality to a federal appeals court in Washington, in hopes of undoing the FCC's move last year to repeal its own rules for Internet service providers. The legal briefs reflect a widening front in the multipronged campaign by consumer groups and tech companies to rescue the ISP regulations, which originally barred providers from blocking websites or slowing them. With the FCC's changes, Internet providers may legally manipulate Internet traffic as it travels over their infrastructure, as long as they disclose their practices to consumers. The FCC's decision last year to repeal the rules was "arbitrary and capricious," said officials from the state of New York, the California Public Utilities Commission and others in court documents Monday -- asking the U.S. Court of Appeals for the District of Columbia Circuit to overrule the agency. The FCC was too credulous in accepting industry promises "to refrain from harmful practices," the officials said, "notwithstanding substantial record evidence showing that [Internet] providers have abused and will abuse their gatekeeper roles in ways that harm consumers and threaten public safety."

Strap on a virtual reality headset and start shopping as if you were in a Walmart. That's a possibility conjured by two patent applications the company filed. The Bentonville, Ark.-based retailer has applied for patents that envision a virtual showroom and a fulfillment center that would allow people to put on a headset and shop in a digital version of the store. Customers could browse and interact with merchandise through a 3D simulation that responds to their gestures, the filings indicate. And in turn, the simulations could generate sensory feedback such as the feeling of moisture, heat, force and wind, as users manipulate the items.

My spine still shivers when I remember the nuclear stand-off between the Soviet Union and the United States in 1962. As a nine-year-old I felt helpless in the face of two leaders poised to push the button. It was MAD – mutually assured destruction – but sanity prevailed and by the end of the 1960s we had détente. In the decades since I have felt comfortable with the dazzling march of technology that has reduced global poverty, given us longer lives, delivered the information superhighway and created my zero-emissions Tesla. Yes, there are disappointments – the internet, for example, has not raised the calibre of conversation but instead has created echo chambers of bigotry and forums for lies and harassment. But now for the first time since the 1960s something is tickling my worry beads: artificial intelligence.

In May 2018, Amnesty International, Access Now, and a handful of partner organizations launched the Toronto Declaration on protecting the right to equality and non-discrimination in machine learning systems. The Declaration is a landmark document that seeks to apply existing international human rights standards to the development and use of machine learning systems (or "artificial intelligence"). Machine learning (ML) is a subset of artificial intelligence. It can be defined as " provid[ing] systems the ability to automatically learn and improve from experience without being explicitly programmed." One of the most significant risks with machine learning is the danger of amplifying existing bias and discrimination against certain groups who already struggle to be treated with dignity and respect.

As a teenager in Nigeria, I tried to build an artificial intelligence system. I was inspired by the same dream that motivated the pioneers in the field: That we could create an intelligence of pure logic and objectivity that would free humanity from human error and human foibles. I was working with weak computer systems and intermittent electricity, and needless to say my AI project failed. Eighteen years later--as an engineer researching artificial intelligence, privacy and machine-learning algorithms--I'm seeing that so far, the premise that AI can free us from subjectivity or bias is also disappointing. We are creating intelligence in our own image.

I've spent the last 8 months going out and pitching big ideas for artificial intelligence solutions. I'm very frequently faced with business people who have been schooled for the last decade on the importance of data. However, this means my services often get conflated with data analytics and big-data consulting. From the business person's perspective, their ask is simple: "We have all this big-data. Can you come in and make us more money from it?"

There has been a surge in applications of machine learning over the last few years as companies look for ways to leverage big data in their products and services. That has corresponded with a big increase in another type of machine learning application – i.e. those sent to the United States Patent and Trademark Office for protection. But the ramifications of the machine learning-patent uptick are not yet clear. Statistical and anecdotal evidence suggests we're in the midst of major upswing in patent protection requests for machine learning inventions. While hard numbers can be tough to come by due to intricacies of the USPTO process (and the fact that it will conceal applications upon request), several researchers have identified what they see as a surge in interest in protecting machine learning products over the past several years.

Are you average in every way, or do you sometimes stand out from the crowd? Your answer might have big implications for how you're treated by the algorithms that governments and corporations are deploying to make important decisions affecting your life. "What algorithms?" you might ask. The ones that decide whether you get hired or fired, whether you're targeted for debt recovery and what news you see, for starters. Automated decisions made using statistical processes "will screw [some] people by default, because that's how statistics works," said Dr Julia Powles, an Australian lawyer currently based at New York University's Information Law Institute.

The increasing incorporation of Artificial Intelligence in the form of automated systems into decision-making procedures highlights not only the importance of decision theory for automated systems but also the need for these decision procedures to be explainable to the people involved in them. Traditional realist accounts of explanation, wherein explanation is a relation that holds (or does not hold) eternally between an explanans and an explanandum, are not adequate to account for the notion of explanation required for artificial decision procedures. We offer an alternative account of explanation as used in the context of automated decision-making that makes explanation an epistemic phenomenon, and one that is dependent on context. This account of explanation better accounts for the way that we talk about, and use, explanations and derived concepts, such as `explanatory power', and also allows us to differentiate between reasons or causes on the one hand, which do not need to have an epistemic aspect, and explanations on the other, which do have such an aspect. Against this theoretical backdrop we then review existing approaches to explanation in Artificial Intelligence and Machine Learning, and suggest desiderata which truly explainable decision systems should fulfill.