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Why Google’s AI ethics advisory board immediately imploded

(TECH NEWS) Google announced their new advisory board to examine ethics regarding artificial intelligence, but employees wildly rejected the members involved.

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At the end of March, Google announced that it was creating a board to help the tech giant navigate the ethics of developing artificial intelligence (AI). This board was called the Advanced Technology External Advisory Council (ATEAC) and pulled together eight panelists representing a broad spectrum of expertise.

According to Google’s announcement, the board was to meet four times over the course of 2019 and discuss “complex challenges” that come from the increasing ubiquity of AI like facial recognition and fairness in machine learning, whether or not the company should work on military applications of AI, and how AI might perpetuate economic disparity worldwide.

The council has already been disbanded.

The board’s quick disintegration comes not from its conflicting ethical ideas regarding AI, but rather furor over the politics of the board members themselves.

It turns out that it was disbanded because of who was on the panel, not really what actually happened once the council convened.

One member, Kay Cole James, has a history of making controversial statements about immigrants and the trans community in her position as the President of the Heritage Foundation. Thousands of Google employees petitioned against her inclusion on the high-profile board.

Another board member that employees rejected was Dyan Gibbens the CEO of Trumball, a company that specializes in drones used by the military (or as Google put it “automation…[ in] resilience in energy and defense.”)

These two members in particular were chosen for the ATEAC in order to bring diversity of thought, but their presence seemingly just brought external drama. When other members were asked by the public whether they thought James’ inclusion on an ethical board was… well, ethical… instead of falling into rank and file, Joanna Bryson replied that she knew even worse information about another (unnamed) board member.

The ATEAC immediately began to lose members and n April 4th, Google announced, “It’s become clear that in the current environment, ATEAC can’t function as we wanted. So we’re ending the council and going back to the drawing board.”

If you put the differing ideologies of its members aside, having only four meetings over the course of the year didn’t really give the organization a lot of time to deeply engage with the weighty topics it was supposed to cover. And, as Vox astutely observes, its unpaid members could only come from a very rarified pool of already wealthy candidates.

This effectively ended the whole hullabaloo, but it begs the question: How exactly was the ATEAC supposed to function?

AprilJo Murphy is a Staff Writer at The American Genius and holds a PhD in English and Creative Writing from the University of North Texas. She is a writer, editor, and sometimes teacher based in Austin, TX who enjoys getting outdoors with her handsome dog, Roan.

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Career consultants help job seekers beat AI robot interviews

(TECH NEWS) With the growth of artificial intelligence conducting the job screening, consultants in South Korea have come up with an innovative response.

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When it comes to resume screenings, women and people of color are regularly passed over, even if they have the exact same resume as a man. In order to give everyone a fair try, we need a system that’s less biased. With the cool, calculating depictions of artificial intelligence in modern media, it’s tempting to say that AI could help us solve our resume screening woes. After all, nothing says unbiased like a machine…right?

Wrong.

I mean, if you need an example of what can go wrong with AI, look no further than Microsoft’s Tay, which went from making banal conversation to spouting racist and misogynistic nonsense in less than 24 hours. Not exactly the ideal.

Sure, Tay was learning from Twitter, which is a hotbed of cruelty and conflict, but the thing is, professional software isn’t always much better. Google’s software has been caught offering biased translations (assuming, for example, if you wrote “engineer” you were referring to a man) and Amazon has been called out for using job screening software that was biased against women.

And that’s just part of what could go wrong with AI scanning your resume. After all, even if gender and race are accounted for (which, again, all bets are off), you’d better bet there are other things – like specific phrases – that these machines are on the lookout for.

So, how do you stand out when it’s a machine, not a human, judging your work? Consultants in South Korea have a solution: teach people how to work around the bots. This includes anything from resume work to learning what facial expressions are ideal for filmed interviews.

It helps that many companies use the same software to do screening. Instead of trying to prepare to impress a wide variety of humans, if someone knew the right tricks for handling an AI system, they could potentially put in much less work. For example, maybe one human interviewer likes big smiles, while the other is put off by them. The AI system, on the other hand, won’t waver from company to company.

Granted, this solution isn’t foolproof either. Not every business uses the same program to scan applicants, for instance. Plus, this tech is still in its relative infancy – a program could easily be in flux as requirements are tweaked. Who knows, maybe someday we’ll actually have application software that can more accurately serve as a judge of applicant quality.

In the meantime, there’s always AI interview classes.

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Google chrome: The anti-cookie monster in 2022

(TECH NEWS) If you are tired of third party cookies trying to grab every bit of data about you, google has heard and responded with their new updates.

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Google has announced the end of third-party tracking cookies on its Chrome browser within the next two years in an effort to grant users better means of security and privacy. With third-party cookies having been relied upon by advertising and social media networks, this move will undoubtedly have ramifications on the digital ad sector.

Google’s announcement was made in a blog post by Chrome engineering director, Justin Schuh. This follows Google’s Privacy Sandbox launch back in August, an initiative meant to brainstorm ideas concerning behavioral advertising online without using third-party cookies.

Chrome is currently the most popular browser, comprising of 64% of the global browser market. Additionally, Google has staked out its role as the world’s largest online ad company with countless partners and intermediaries. This change and any others made by Google will affect this army of partnerships.

This comes in the wake of rising popularity for anti-tracking features on web browsers across the board. Safari and Firefox have both launched updates (Intelligent Tracking Prevention for Safari and the Enhanced Tracking Prevention for Firefox) with Microsoft having recently released the new Edge browser which automatically utilizes tracking prevention. These changes have rocked share prices for ad tech companies since last year.

The two-year grace period before Chrome goes cookie-less has given the ad and media industries time to absorb the shock and develop plans of action. The transition has soften the blow, demonstrating Google’s willingness to keep positive working relations with ad partnerships. Although users can look forward to better privacy protection and choice over how their data is used, Google has made it clear it’s trying to keep balance in the web ecosystems which will likely mean compromises for everyone involved.

Chrome’s SameSite cookie update will launch in February, requiring publishers and ad tech vendors to label third-party cookies that can be used elsewhere on the web.

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Computer vision helps AI create a recipe from just a photo

(TECH NEWS) It’s so hard to find the right recipe for that beautiful meal you saw on tv or online. Well computer vision helps AI recreate it from a picture!

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Ever seen at a photo of a delicious looking meal on Instagram and wondered how the heck to make that? Now there’s an AI for that, kind of.

Facebook’s AI research lab has been developing a system that can analyze a photo of food and then create a recipe. So, is Facebook trying to take on all the food bloggers of the world now too?

Well, not exactly, the AI is part of an ongoing effort to teach AI how to see and then understand the visual world. Food is just a fun and challenging training exercise. They have been referring to it as “inverse cooking.”

According to Facebook, “The “inverse cooking” system uses computer vision, technology that extracts information from digital images and videos to give computers a high level of understanding of the visual world,”

The concept of computer vision isn’t new. Computer vision is the guiding force behind mobile apps that can identify something just by snapping a picture. If you’ve ever taken a photo of your credit card on an app instead of typing out all the numbers, then you’ve seen computer vision in action.

Facebook researchers insist that this is no ordinary computer vision because their system uses two networks to arrive at the solution, therefore increasing accuracy. According to Facebook research scientist Michal Drozdzal, the system works by dividing the problem into two parts. A neutral network works to identify ingredients that are visible in the image, while the second network pulls a recipe from a kind of database.

These two networks have been the key to researcher’s success with more complicated dishes where you can’t necessarily see every ingredient. Of course, the tech team hasn’t stepped foot in the kitchen yet, so the jury is still out.

This sounds neat and all, but why should you care if the computer is learning how to cook?

Research projects like this one carry AI technology a long way. As the AI gets smarter and expands its limits, researchers are able to conceptualize new ways to put the technology to use in our everyday lives. For now, AI like this is saving you the trouble of typing out your entire credit card number, but someday it could analyze images on a much grander scale.

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