America’s largest companies push for federal online privacy laws to circumvent state regulatory efforts – gpgmail


As California moves ahead with what would be the most restrictive online privacy laws in the nation, the chief executives of some of the nation’s largest companies are taking their case to the nation’s capitol to plead for federal regulation.

Chief executives at Amazon, AT&T, Dell, Ford, IBM, Qualcomm, Walmart, and other leading financial services, manufacturing, and technology companies have issued an open letter to Congressional leadership pleading with them to take action on online privacy, through the pro-industry organization, The Business Roundtable.

“Now is the time for Congress to act and ensure that consumers are not faced with confusion about their rights and protections based on a patchwork of inconsistent state laws. Further, as the regulatory landscape becomes increasingly fragmented and more complex, U.S. innovation and global competitiveness in the digital economy are threatened,” the letter says.

The subtext to this call to action is the California privacy regulations that are set to take effect by the end of this year.

As we noted when the bill was passed last year there are a few key components of the California legislation including the following requirements:

  • Businesses must disclose what information they collect, what business purpose they do so for and any third parties they share that data with.

  • Businesses would be required to comply with official consumer requests to delete that data.

  • Consumers can opt out of their data being sold, and businesses can’t retaliate by changing the price or level of service.

  • Businesses can, however, offer “financial incentives” for being allowed to collect data.

  • California authorities are empowered to fine companies for violations.

There’s a reason why companies would push for federal regulation to supersede any initiatives from the states. It is more of a challenge for companies to adhere to a patchwork of different regulatory regimes at the state level. But it’s also true that companies, following the lead of automakers in California, could just adhere to the most stringent requirements which would clarify any confusion.

Indeed many of these companies are already complying with strict privacy regulations thanks to the passage of the GDPR in Europe.


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Forty-nine states and the District of Columbia are pushing an antitrust investigation against Google – gpgmail


Fifty attorneys general are pushing forward with an antitrust investigation against Google, led by the Texas state Attorney General Ken Paxton.

In an announcement on the steps of the U.S. Supreme Court building, Paxton and a gathering of attorneys general said that the focus of the investigation would be on Google’s advertising practices, but that other points of inquiry could be included in the investigation.

The investigation into Google comes as big technology companies find themselves increasingly under the regulatory microscope for everything from anticompetitive business practices to violations of users’ privacy and security, to accusations of political bias.

Last week, the New York State Attorney General launched an investigation into Facebook.

Action from the states follows movement from the federal government which is investigating just about every major technology company including Google, Apple, Amazon, and Facebook.

This story is developing.




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Adarga closes £5M Series A funding for its Palantir-like AI platform – gpgmail


AI startup Adarga has closed a £5 million Series A fundraising by Allectus Capital. But this news rather cloaks the fact that it’s been building up a head of steam since it’s founding in 2016, building up – what they say – is a £30 million-plus sales pipeline through strategic collaborations with a number of global industrial partners and gradually building its management team.

The proceeds will be used to continue the expansion of Adarga’s data science and software engineering teams and roll out internationally.

Adarga, which comes from the word for an old Moorish shield, is a London and Bristol-based start-up. It uses AI to change the way financial institutions, intelligence agencies and defence companies tackle problems, helping crunch vast amounts of data to identify possible threats even before they occur. The start-up’s proposition sounds similar to that of Palantir, which is known for working with the US military.

What Adarga does is allow organizations to transform normally data-intensive, human knowledge processes by analyzing vast volumes of data more quickly and accurately. Adarga clients can build up a ‘Knowledge Graph’ about subjects, and targets.

The UK government is a client as well as the finance sector, where it’s used for financial analysis and by insurance companies. Founded in 2016, it now has 26 employees – including data scientists from some of the UK’s top universities.

The company has received support from Benevolent AI, one of the key players in the UK AI tech scene. Benevolent AI, which is worth $2bn after a $115m funding round, is a minority shareholder in Adarga. It has not provided financial backing, but support in kind and technical help.

Rob Bassett Cross, CEO of Adarga, commented: “With the completion of this round, Adarga is focused on consolidating its competitive position in the UK defence and security sector. We are positioning ourselves as the software platform of choice for organisations who cannot deal effectively with the scale and complexity of their enterprise data and are actively seeking an alternative to knowledge intensive human processes. Built by experienced sector specialists, the Company has rapidly progressed a real solution to address the challenges of an ever-growing volume of unstructured data.”

Bassett Cross is an interesting guy, to say the least. You won’t find much about him on LinkedIn, but in previous interviews, he has revealed that he is a former army officer and special operations expert who fought in Iraq and Afghanistan, and was awarded military cross.

The company recently held a new annual event, the Adarga AI Symposium at the The Royal Institution, London, which featured futurist Mark Stevenson, Ranju Das of Amazon Web Services, and General Stanley A. McChrystal.

Matthew Gould, Head of Emerging Technology at Allectus Capital, said: “Adarga has developed a world-class analytics platform to support real-time critical decisioning by public sector and defence stakeholders. What Rob and the team have built in a short time is a hugely exciting example of the founder-led, disruptive businesses that we like to partner with – especially in an ever-increasing global threat landscape.”

Allectus Capital is based in Sydney, Australia and invests across Asia-Pacific, UK and US. It has previously invested in Cluey Learning (Series A, A$20M), Everproof, Switch Automation and Automio.


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Reps from DHS, the FBI and the ODNI met with tech companies at Facebook to talk election security – gpgmail


Representatives from the Federal Bureau of Investigation, the Office of the Director of National Intelligence and the Department of Homeland Security met with counterparts at tech companies including Facebook, Google, Microsoft and Twitter to discuss election security, Facebook confirmed.

The purpose was to build on previous discussions and further strengthen strategic collaboration regarding the security of the 2020 U.S. state, federal, and presidential elections,” according to a statement from Facebook head of cybersecurity policy, Nathaniel Gleicher.

First reported by Bloomberg, the meeting between America’s largest technology companies and the trio of government security agencies responsible for election security is a sign of how seriously the government and the country’s largest technology companies are treating the threat of foreign intervention into elections.

Earlier this year the Office of the Inspector General issued a report saying that the Department of Homeland Security has not done enough to safeguard elections in the United States.

Throughout the year, reports of persistent media manipulation and the dissemination of propaganda on social media platforms have cropped up not just in the United States but around the world.

In April, Facebook removed a number of accounts ahead of the Spanish election for their role in spreading misinformation about the campaign.

Companies have responded to the threat by updating different mechanisms for users to call out fake accounts and improving in-house technologies used to combat the spread of misinformation.

Twitter, for instance, launched a reporting tool whereby users can flag misleading tweets.

“Improving election security and countering information operations are complex challenges that no organization can solve alone,” said Gleicher in a statement. “Today’s meeting builds on our continuing commitment to work with industry and government partners, as well as with civil society and security experts, to better understand emerging threats and prepare for future elections.”


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Apple products under pricing pressure as new 15% tariffs drop Sunday – gpgmail


A new 15% tariff on Chinese imports will go in effect just after midnight Sunday, placing levies on hundreds of household goods and consumer tech, including a bevy of Apple products.

The tariffs, put in place by President Donald Trump as part of an escalating tit-for-tat trade war with China, were entered into the Federal Register on Friday.

Apple, the largest U.S. technology company by market cap, has its products assembled in China by Foxconn and then ships them to consumers all over the world. The Apple Airpods, Apple Watch and accompanying Apple Watch bands and the Apple Homepod are all products subject to the higher tariffs beginning Sunday. The iPhone doesn’t appear to be impacted this round, but could be subject to tariffs that begin Dec. 15.

Apple is hardly the only electronics company — most of which have final assembly in China — to be affected by the tariffs. TVs, speakers, digital cameras, lithium-ion batteries and flash drives are just a few of consumer electronics that will be subjected to a 15% tariff beginning Sunday. But the higher tariffs do threaten to give rival Samsung an edge.

The new higher tariffs come just a few weeks since Apple CEO Tim Cook met with Trump to argue that such a move would benefit its No. 1 competitor Samsung.

The 15% tariff will affect about $112 billion of Chinese goods, lower than the original list of $300 billion imports. Last week, the U.S. Trade Representative office modified the original list, either delaying tariffs on some products until December 15 or removing some goods altogether.

Despite the lower number, the impact is still expected to pinch companies importing products from China. The complete list of products affected by the 15% tariffs is 122 pages long. And eventually, that pain — aka higher prices — will be passed onto consumers.

Apple has not said whether it will increase prices of its products. Analysts from JP Morgan expect Apple to absorb the costs.

Tariffs have already had a cost, according to the Consumer Tech Association. Since July 2018, Section 301 tariffs on China have cost the consumer tech industry over $10 billion, including $1 billion on 5G-related products, the CTA said.

In total, American taxpayers have paid over $27 billion in extra import tariffs from the beginning of the trade war in 2018 through June of this year, most of which can be attributed to the U.S.-China trade war, according to U.S. Census information provided by the Information Technology Industry Council (ITI).

Another 30% tariff on about $250 billion of goods is expected to begin October 1.


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Former Google X ecec Mo Gawdat wants to reinvent consumerism – gpgmail


Mo Gawdat, the former Google and Google X executive, is probably best known for his book Solve for Happy: Engineer Your Path to Joy. He left Google X last year. Quite a bit has been written about the events that led to him leaving Google, including the tragic death of his son. While happiness is still very much at the forefront of what he’s doing, he’s also now thinking about his next startup: T0day.

To talk about T0day, I sat down with the Egypt-born Gawdat at the Digital Frontrunners event in Copenhagen, where he gave one of the keynote presentations. Gawdat is currently based in London. He has adopted a minimalist lifestyle, with no more than a suitcase and a carry-on full of things. Unlike many of the Silicon Valley elite that have recently adopted a kind of performative aestheticism, Gawdat’s commitment to minimalism feels genuine — and it also informs his new startup.

“In my current business, I’m building a startup that is all about reinventing consumerism,” he told me. “The problem with retail and consumerism is it’s never been disrupted. E-commerce, even though we think is a massive revolution, it’s just an evolution and it’s still tiny as a fraction of all we buy. It was built for the Silicon Valley mentality of disruption, if you want, while actually, what you need is cooperation. There are so many successful players out there, so many efficient supply chains. We want the traditional retailers to be successful and continue to make money — even make more money.”

What T0day wants to be is a platform that integrates all of the players in the retail ecosystem. That kind of platform, Gawdat argues, never existed before, “because there was never a platform player.”

That sounds like an efficient marketplace for moving goods, but in Gawdat’s imagination, it is also a way to do good for the planet. Most of the fuel burned today isn’t for moving people, he argues, but goods. A lot of the food we buy goes to waste (together with all of the resources it took to grow and ship it) and single-use plastic remains a scourge.

How does T0day fix that? Gawdat argues that today’s e-commerce is nothing but a digital rendering of the same window shopping people have done for ages. “You have to reimagine what it’s like to consume,” he said.

The reimagined way to consume is essentially just-in-time shipping for food and other consumer goods, based on efficient supply chains that outsmart today’s hub and spoke distribution centers and can deliver anything to you in half an hour. If everything you need to cook a meal arrives 15 minutes before you want to start cooking, you only need to order the items you need at that given time and instead of a plastic container, it could come a paper bag. “If I have the right robotics and the right autonomous movements — not just self-driving cars, because self-driving cars are a bit far away — but the right autonomous movements within the enterprise space of the warehouse, I could literally give it to you with the predictability of five minutes within half an hour,” he explained. “If you get everything you need within half an hour, why would you need to buy seven apples? You would buy three.”

Some companies, including the likes of Uber, are obviously building some of the logistics networks that will enable this kind of immediate drop shipping, but Gawdat doesn’t think Uber is the right company for this. “This is going to sound a little spiritual. There is what you do and there is the intention behind why you do it,” he said. “You can do the exact same thing with a different intention and get a very different result.”

That’s an ambitious project, but Gawdat argues that it can be done without using massive amounts of resources. Indeed, he argues that one of the problems with Google X, and especially big moonshot projects like Loon and self-driving cars, was that they weren’t really resource-constrained. “Some things took longer than they should have,” he said. “But I don’t criticize what they did at all. Take the example of Loon and Facebook. Loon took longer than it should have. In my view, it was basically because of an abundance of resources and sometimes innovation requires a shoestring. That’s my only criticism.”

T0day, which Gawdat hasn’t really talked about publicly in the past, is currently self-funded. A lot of people are advising him to raise money for it. “We’re getting a lot of advice that we shouldn’t self-fund,” he said, but he also believes that the company will need some strategic powerhouses on its side, maybe retailers or companies that have already invested in other components of the overall platform.

T0day’s ambitions are massive, but Gawdat thinks that his team can get the basic elements right, be that the fulfillment center design or the routing algorithms and the optimization engines that power it all. He isn’t ready to talk about those, though. What he does think is that T0day won’t be the interface for these services. It’ll be the back end and allow others to build on top. And because his previous jobs have allowed him to live a comfortable life, he isn’t all that worried about margins either, and would actually be happy if others adopted his idea, thereby reducing waste.


10 minutes mail – Also known by names like : 10minemail, 10minutemail, 10mins email, mail 10 minutes, 10 minute e-mail, 10min mail, 10minute email or 10 minute temporary email. 10 minute email address is a disposable temporary email that self-destructed after a 10 minutes. https://tempemail.co/– is most advanced throwaway email service that helps you avoid spam and stay safe. Try tempemail and you can view content, post comments or download something

Former Google X exec Mo Gawdat wants to reinvent consumerism – gpgmail


Mo Gawdat, the former Google and Google X executive, is probably best known for his book Solve for Happy: Engineer Your Path to Joy. He left Google X last year. Quite a bit has been written about the events that led to him leaving Google, including the tragic death of his son. While happiness is still very much at the forefront of what he’s doing, he’s also now thinking about his next startup: T0day.

To talk about T0day, I sat down with the Egypt-born Gawdat at the Digital Frontrunners event in Copenhagen, where he gave one of the keynote presentations. Gawdat is currently based in London. He has adopted a minimalist lifestyle, with no more than a suitcase and a carry-on full of things. Unlike many of the Silicon Valley elite that have recently adopted a kind of performative aestheticism, Gawdat’s commitment to minimalism feels genuine — and it also informs his new startup.

“In my current business, I’m building a startup that is all about reinventing consumerism,” he told me. “The problem with retail and consumerism is it’s never been disrupted. E-commerce, even though we think is a massive revolution, it’s just an evolution and it’s still tiny as a fraction of all we buy. It was built for the Silicon Valley mentality of disruption, if you want, while actually, what you need is cooperation. There are so many successful players out there, so many efficient supply chains. We want the traditional retailers to be successful and continue to make money — even make more money.”

What T0day wants to be is a platform that integrates all of the players in the retail ecosystem. That kind of platform, Gawdat argues, never existed before, “because there was never a platform player.”

That sounds like an efficient marketplace for moving goods, but in Gawdat’s imagination, it is also a way to do good for the planet. Most of the fuel burned today isn’t for moving people, he argues, but goods. A lot of the food we buy goes to waste (together with all of the resources it took to grow and ship it) and single-use plastic remains a scourge.

How does T0day fix that? Gawdat argues that today’s e-commerce is nothing but a digital rendering of the same window shopping people have done for ages. “You have to reimagine what it’s like to consume,” he said.

The reimagined way to consume is essentially just-in-time shipping for food and other consumer goods, based on efficient supply chains that outsmart today’s hub and spoke distribution centers and can deliver anything to you in half an hour. If everything you need to cook a meal arrives 15 minutes before you want to start cooking, you only need to order the items you need at that given time and instead of a plastic container, it could come a paper bag. “If I have the right robotics and the right autonomous movements — not just self-driving cars, because self-driving cars are a bit far away — but the right autonomous movements within the enterprise space of the warehouse, I could literally give it to you with the predictability of five minutes within half an hour,” he explained. “If you get everything you need within half an hour, why would you need to buy seven apples? You would buy three.”

Some companies, including the likes of Uber, are obviously building some of the logistics networks that will enable this kind of immediate drop shipping, but Gawdat doesn’t think Uber is the right company for this. “This is going to sound a little spiritual. There is what you do and there is the intention behind why you do it,” he said. “You can do the exact same thing with a different intention and get a very different result.”

That’s an ambitious project, but Gawdat argues that it can be done without using massive amounts of resources. Indeed, he argues that one of the problems with Google X, and especially big moonshot projects like Loon and self-driving cars, was that they weren’t really resource-constrained. “Some things took longer than they should have,” he said. “But I don’t criticize what they did at all. Take the example of Loon and Facebook. Loon took longer than it should have. In my view, it was basically because of an abundance of resources and sometimes innovation requires a shoestring. That’s my only criticism.”

T0day, which Gawdat hasn’t really talked about publicly in the past, is currently self-funded. A lot of people are advising him to raise money for it. “We’re getting a lot of advice that we shouldn’t self-fund,” he said, but he also believes that the company will need some strategic powerhouses on its side, maybe retailers or companies that have already invested in other components of the overall platform.

T0day’s ambitions are massive, but Gawdat thinks that his team can get the basic elements right, be that the fulfillment center design or the routing algorithms and the optimization engines that power it all. He isn’t ready to talk about those, though. What he does think is that T0day won’t be the interface for these services. It’ll be the back end and allow others to build on top. And because his previous jobs have allowed him to live a comfortable life, he isn’t all that worried about margins either, and would actually be happy if others adopted his idea, thereby reducing waste.


10 minutes mail – Also known by names like : 10minemail, 10minutemail, 10mins email, mail 10 minutes, 10 minute e-mail, 10min mail, 10minute email or 10 minute temporary email. 10 minute email address is a disposable temporary email that self-destructed after a 10 minutes. https://tempemail.co/– is most advanced throwaway email service that helps you avoid spam and stay safe. Try tempemail and you can view content, post comments or download something

Zhiyun’s Smooth-Q2 aims to be the most portable quality smartphone gimbal available – gpgmail


Zhiyun has been steadily rolling out new gimbals for smartphones and dedicated cameras for a few years now, and the company’s quality and feature set has improved dramatically over time. Now, it’s launching the Zhiyun Smooth-Q2 smartphone gimbal on Kickstarter, with the aim of delivering a “truly pocket-size” gimbal that has all the bells and whistles you could ever want or need.

The Smooth-Q2 is indeed a portable powerhouse – the company sent me a pre-production unit to test, and though it’s not the final shipping hardware, it already works and feels like a polished, quality device. The first thing you’ll notice right away about the Smooth-Q2 is its size – it can indeed slip inside a coat or pant pocket, though you’ll need a fairly deep one to make that work. Even if you don’t necessarily have a compatible pocket, it’s hard to beat the Smooth-Q2 for sheer portability, and that’s bound to save you some packing space when you’re getting ready for your next trip.

There’s another recently released small-size smartphone gimbal on the market – the DJI Osmo Mobile 3. That has a clever method of folding down for easier packing, but the Smooth-Q2’s design, while similar in overall footprint, means it’s much easier to put in your actual pocket (or pack in a bag’s side pocket) than is the DJI version. And while both are incredibly easy to balance even if you’re a gimbal novice, I found the Zhiyun was actually the simpler of the two.

The Zhiyun Smooth-Q2 also feels more solidly constructed, though its simpler controls (it doesn’t have a trigger around or a zoom lever) may leave some creators wanting. There are some other advantages here, too, however – a quick release spring-loaded clip means you can detach your smartphone quickly for other uses without unbalancing the gimbal, and go right back to shooting when you’re done. Plus, you can connect via Bluetooth and control your smartphone’s native camera app directly, instead of relying on their ZP Play app – which you can still use for features like object tracking.

The Smooth-Q2 offers 16-hours of battery life, so you should easily make it through a day without requiring power, and it can do time lapses, with or without programmed motion, a vortex mode for capturing crazy rotational footage, and an aluminum body that should be able to withstand less-than careful stowage in your bag.

In terms of quality, the Smooth-Q2 really delivers in early testing with my iPhone XS Max, and I’ve included two quick sample clips so you can see for yourself. These are shot in the gimbal’s basic PF mode, in which the camera pans as you turn the gimbal side to side.

Zhiyun’s crowdfunding these but the company’s history and reputation mean that you can count on them to deliver. The entry-level price is set at $109 U.S. for backers, which is a $30 discount off the planned retail cost, and they should ship to backers in October according to the company.

Smooth Q2 2


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The risks of amoral A.I. – gpgmail


Artificial intelligence is now being used to make decisions about lives, livelihoods, and interactions in the real world in ways that pose real risks to people.

We were all skeptics once. Not that long ago, conventional wisdom held that machine intelligence showed great promise, but it was always just a few years away. Today there is absolute faith that the future has arrived.

It’s not that surprising with cars that (sometimes and under certain conditions) drive themselves and software that beats humans at games like chess and Go. You can’t blame people for being impressed.

But board games, even complicated ones, are a far cry from the messiness and uncertainty of real-life, and autonomous cars still aren’t actually sharing the road with us (at least not without some catastrophic failures).

AI is being used in a surprising number of applications, making judgments about job performance, hiring, loans, and criminal justice among many others. Most people are not aware of the potential risks in these judgments. They should be. There is a general feeling that technology is inherently neutral — even among many of those developing AI solutions. But AI developers make decisions and choose tradeoffs that affect outcomes. Developers are embedding ethical choices within the technology but without thinking about their decisions in those terms.

These tradeoffs are usually technical and subtle, and the downstream implications are not always obvious at the point the decisions are made.

The fatal Uber accident in Tempe, Arizona, is a (not-subtle) but good illustrative example that makes it easy to see how it happens.

The autonomous vehicle system actually detected the pedestrian in time to stop but the developers had tweaked the emergency braking system in favor of not braking too much, balancing a tradeoff between jerky driving and safety. The Uber developers opted for the more commercially viable choice. Eventually autonomous driving technology will improve to a point that allows for both safety and smooth driving, but will we put autonomous cars on the road before that happens? Profit interests are pushing hard to get them on the road immediately.

Physical risks pose an obvious danger, but there has been real harm from automated decision-making systems as well. AI does, in fact, have the potential to benefit the world. Ideally, we mitigate for the downsides in order to get the benefits with minimal harm.

A significant risk is that we advance the use of AI technology at the cost of reducing individual human rights. We’re already seeing that happen. One important example is that the right to appeal judicial decisions is weakened when AI tools are involved. In many other cases, individuals don’t even know that a choice not to hire, promote, or extend a loan to them was informed by a statistical algorithm. 

Buyer Beware

Buyers of the technology are at a disadvantage when they know so much less about it than the sellers do. For the most part decision makers are not equipped to evaluate intelligent systems. In economic terms, there is an information asymmetry that puts AI developers in a more powerful position over those who might use it. (Side note: the subjects of AI decisions generally have no power at all.) The nature of AI is that you simply trust (or not) the decisions it makes. You can’t ask technology why it decided something or if it considered other alternatives or suggest hypotheticals to explore variations on the question you asked. Given the current trust in technology, vendors’ promises about a cheaper and faster way to get the job done can be very enticing.

So far, we as a society have not had a way to assess the value of algorithms against the costs they impose on society. There has been very little public discussion even when government entities decide to adopt new AI solutions. Worse than that, information about the data used for training the system plus its weighting schemes, model selection, and other choices vendors make while developing the software are deemed trade secrets and therefore not available for discussion.

Image via Getty Images / sorbetto

The Yale Journal of Law and Technology published a paper by Robert Brauneis and Ellen P. Goodman where they describe their efforts to test the transparency around government adoption of data analytics tools for predictive algorithms. They filed forty-two open records requests to various public agencies about their use of decision-making support tools.

Their “specific goal was to assess whether open records processes would enable citizens to discover what policy judgments these algorithms embody and to evaluate their utility and fairness”. Nearly all of the agencies involved were either unwilling or unable to provide information that could lead to an understanding of how the algorithms worked to decide citizens’ fates. Government record-keeping was one of the biggest problems, but companies’ aggressive trade secret and confidentiality claims were also a significant factor.

Using data-driven risk assessment tools can be useful especially in cases identifying low-risk individuals who can benefit from reduced prison sentences. Reduced or waived sentences alleviate stresses on the prison system and benefit the individuals, their families, and communities as well. Despite the possible upsides, if these tools interfere with Constitutional rights to due process, they are not worth the risk.

All of us have the right to question the accuracy and relevance of information used in judicial proceedings and in many other situations as well. Unfortunately for the citizens of Wisconsin, the argument that a company’s profit interest outweighs a defendant’s right to due process was affirmed by that state’s supreme court in 2016.

Fairness is in the Eye of the Beholder

Of course, human judgment is biased too. Indeed, professional cultures have had to evolve to address it. Judges for example, strive to separate their prejudices from their judgments, and there are processes to challenge the fairness of judicial decisions.

In the United States, the 1968 Fair Housing Act was passed to ensure that real-estate professionals conduct their business without discriminating against clients. Technology companies do not have such a culture. Recent news has shown just the opposite. For individual AI developers, the focus is on getting the algorithms correct with high accuracy for whatever definition of accuracy they assume in their modeling.

I recently listened to a podcast where the conversation wondered whether talk about bias in AI wasn’t holding machines to a different standard than humans—seeming to suggest that machines were being put at a disadvantage in some imagined competition with humans.

As true technology believers, the host and guest eventually concluded that once AI researchers have solved the machine bias problem, we’ll have a new, even better standard for humans to live up to, and at that point the machines can teach humans how to avoid bias. The implication is that there is an objective answer out there, and while we humans have struggled to find it, the machines can show us the way. The truth is that in many cases there are contradictory notions about what it means to be fair.

A handful of research papers have come out in the past couple of years that tackle the question of fairness from a statistical and mathematical point-of-view. One of the papers, for example, formalizes some basic criteria to determine if a decision is fair.

In their formalization, in most situations, differing ideas about what it means to be fair are not just different but actually incompatible. A single objective solution that can be called fair simply doesn’t exist, making it impossible for statistically trained machines to answer these questions. Considered in this light, a conversation about machines giving human beings lessons in fairness sounds more like theater of the absurd than a purported thoughtful conversation about the issues involved.

Image courtesy of gpgmail/Bryce Durbin

When there are questions of bias, a discussion is necessary. What it means to be fair in contexts like criminal sentencing, granting loans, job and college opportunities, for example, have not been settled and unfortunately contain political elements. We’re being asked to join in an illusion that artificial intelligence can somehow de-politicize these issues. The fact is, the technology embodies a particular stance, but we don’t know what it is.

Technologists with their heads down focused on algorithms are determining important structural issues and making policy choices. This removes the collective conversation and cuts off input from other points-of-view. Sociologists, historians, political scientists, and above all stakeholders within the community would have a lot to contribute to the debate. Applying AI for these tricky problems paints a veneer of science that tries to dole out apolitical solutions to difficult questions. 

Who Will Watch the (AI) Watchers?

One major driver of the current trend to adopt AI solutions is that the negative externalities from the use of AI are not borne by the companies developing it. Typically, we address this situation with government regulation. Industrial pollution, for example, is restricted because it creates a future cost to society. We also use regulation to protect individuals in situations where they may come to harm.

Both of these potential negative consequences exist in our current uses of AI. For self-driving cars, there are already regulatory bodies involved, so we can expect a public dialog about when and in what ways AI driven vehicles can be used. What about the other uses of AI? Currently, except for some action by New York City, there is exactly zero regulation around the use of AI. The most basic assurances of algorithmic accountability are not guaranteed for either users of technology or the subjects of automated decision making.

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Image via Getty Images / nadia_bormotova

Unfortunately, we can’t leave it to companies to police themselves. Facebook’s slogan, “Move fast and break things” has been retired, but the mindset and the culture persist throughout Silicon Valley. An attitude of doing what you think is best and apologizing later continues to dominate.

This has apparently been effective when building systems to upsell consumers or connect riders with drivers. It becomes completely unacceptable when you make decisions affecting people’s lives. Even if well-intentioned, the researchers and developers writing the code don’t have the training or, at the risk of offending some wonderful colleagues, the inclination to think about these issues.

I’ve seen firsthand too many researchers who demonstrate a surprising nonchalance about the human impact. I recently attended an innovation conference just outside of Silicon Valley. One of the presentations included a doctored video of a very famous person delivering a speech that never actually took place. The manipulation of the video was completely imperceptible.

When the researcher was asked about the implications of deceptive technology, she was dismissive of the question. Her answer was essentially, “I make the technology and then leave those questions to the social scientists to work out.” This is just one of the worst examples I’ve seen from many researchers who don’t have these issues on their radars. I suppose that requiring computer scientists to double major in moral philosophy isn’t practical, but the lack of concern is striking.

Recently we learned that Amazon abandoned an in-house technology that they had been testing to select the best resumes from among their applicants. Amazon discovered that the system they created developed a preference for male candidates, in effect, penalizing women who applied. In this case, Amazon was sufficiently motivated to ensure their own technology was working as effectively as possible, but will other companies be as vigilant?

As a matter of fact, Reuters reports that other companies are blithely moving ahead with AI for hiring. A third-party vendor selling such technology actually has no incentive to test that it’s not biased unless customers demand it, and as I mentioned, decision makers are mostly not in a position to have that conversation. Again, human bias plays a part in hiring too. But companies can and should deal with that.

With machine learning, they can’t be sure what discriminatory features the system might learn. Absent the market forces, unless companies are compelled to be transparent about the development and their use of opaque technology in domains where fairness matters, it’s not going to happen.

Accountability and transparency are paramount to safely using AI in real-world applications. Regulations could require access to basic information about the technology. Since no solution is completely accurate, the regulation should allow adopters to understand the effects of errors. Are errors relatively minor or major? Uber’s use of AI killed a pedestrian. How bad is the worst-case scenario in other applications? How are algorithms trained? What data was used for training and how was it assessed to determine its fitness for the intended purpose? Does it truly represent the people under consideration? Does it contain biases? Only by having access to this kind of information can stakeholders make informed decisions about appropriate risks and tradeoffs.

At this point, we might have to face the fact that our current uses of AI are getting ahead of its capabilities and that using it safely requires a lot more thought than it’s getting now.


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Postmates lands permit to test its Serve autonomous delivery robots in SF – gpgmail


Postmates has officially received the green light from the city of San Francisco to begin testing its Serve wheeled delivery robot on city streets, as first reported by the SF Chronicle and confirmed with Postmates by gpgmail. The on-demand delivery company told us last week that it expected the issuance of the permit to come through shortly after a conditional approval, and that’s exactly what happened on Wednesday this week.

The permit doesn’t cover the entire city — just a designated area of a number of blocks in and around Potrero Hill and the Inner Mission, but it will allow Postmates to begin testing up to three autonomous delivery robots at once, at speeds of up to 3 mph. Deliveries can only take place between 8 AM and 6:30 PM on weekdays, and a human has to be on hand within 30 feet of the vehicles while they’re operating. Still, it’s a start — and from a city regulatory environment that has had a somewhat rocky start with some less collaborative early pilots from other companies.

Autonomous delivery bot company Marble also has a permit application pending with the city’s Public Works department, and will look to test its own four-wheeled, sensor-equipped rolling delivery bots within the city soon, should it be granted similar testing approval.

Postmates revealed Serve last December, taking a more anthropomorphic approach to the vehicle’s overall design. Like many short-distance delivery robots of its ilk, it includes a lockable cargo container and screen-based user interface for eventual autonomous deliveries to customers. The competitive field for autonomous rolling delivery bots is growing continuously, with companies like Starship Technologies, Amazon and many more throwing their hats in the ring.


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