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The US’s online language gaps are an urgent problem for Asian-Americans



The US’s online language gaps are an urgent problem for Asian-Americans

Chen says that while content moderation policies from Facebook, Twitter, and others succeeded in filtering out some of the most obvious English-language disinformation, the system often misses such content when it’s in other languages. That work instead had to be done by volunteers like her team, who looked for disinformation and were trained to defuse it and minimize its spread. “Those mechanisms meant to catch certain words and stuff don’t necessarily catch that dis- and misinformation when it’s in a different language,” she says.

Google’s translation services and technologies such as Translatotron and real-time translation headphones use artificial intelligence to convert between languages. But Xiong finds these tools inadequate for Hmong, a deeply complex language where context is incredibly important. “I think we’ve become really complacent and dependent on advanced systems like Google,” she says. “They claim to be ‘language accessible,’ and then I read it and it says something totally different.” 

(A Google spokesperson admitted that smaller languages “pose a more difficult translation task” but said that the company has “invested in research that particularly benefits low-resource language translations,” using machine learning and community feedback.)

All the way down

The challenges of language online go beyond the US—and down, quite literally, to the underlying code. Yudhanjaya Wijeratne is a researcher and data scientist at the Sri Lankan think tank LIRNEasia. In 2018, he started tracking bot networks whose activity on social media encouraged violence against Muslims: in February and March of that year, a string of riots by Sinhalese Buddhists targeted Muslims and mosques in the cities of Ampara and Kandy. His team documented “the hunting logic” of the bots, catalogued hundreds of thousands of Sinhalese social media posts, and took the findings to Twitter and Facebook. “They’d say all sorts of nice and well-meaning things–basically canned statements,” he says. (In a statement, Twitter says it uses human review and automated systems to “apply our rules impartially for all people in the service, regardless of background, ideology, or placement on the political spectrum.”)

When contacted by MIT Technology Review, a Facebook spokesperson said the company commissioned an independent human rights assessment of the platform’s role in the violence in Sri Lanka, which was published in May 2020, and made changes in the wake of the attacks, including hiring dozens of Sinhala and Tamil-speaking content moderators. “We deployed proactive hate speech detection technology in Sinhala to help us more quickly and effectively identify potentially violating content,” they said.

“What I can do with three lines of code in Python in English literally took me two years of looking at 28 million words of Sinhala”

Yudhanjaya Wijeratne, LIRNEasia

When the bot behavior continued, Wijeratne grew skeptical of the platitudes. He decided to look at the code libraries and software tools the companies were using, and found that the mechanisms to monitor hate speech in most non-English languages had not yet been built. 

“Much of the research, in fact, for a lot of languages like ours has simply not been done yet,” Wijeratne says. “What I can do with three lines of code in Python in English literally took me two years of looking at 28 million words of Sinhala to build the core corpuses, to build the core tools, and then get things up to that level where I could potentially do that level of text analysis.”

After suicide bombers targeted churches in Colombo, the Sri Lankan capital, in April 2019, Wijeratne built a tool to analyze hate speech and misinformation in Sinhala and Tamil. The system, called Watchdog, is a free mobile application that aggregates news and attaches warnings to false stories. The warnings come from volunteers who are trained in fact-checking. 

Wijeratne stresses that this work goes far beyond translation. 

“Many of the algorithms that we take for granted that are often cited in research, in particular in natural-language processing, show excellent results for English,” he says. “And yet many identical algorithms, even used on languages that are only a few degrees of difference apart—whether they’re West German or from the Romance tree of languages—may return completely different results.” 

Natural-language processing is the basis of automated content moderation systems. Wijeratne published a paper in 2019 that examined the discrepancies between their accuracy in different languages. He argues that the more computational resources that exist for a language, like data sets and web pages, the better the algorithms can work. Languages from poorer countries or communities are disadvantaged.

“If you’re building, say, the Empire State Building for English, you have the blueprints. You have the materials,” he says. “You have everything on hand and all you have to do is put this stuff together. For every other language, you don’t have the blueprints.

“You have no idea where the concrete is going to come from. You don’t have steel and you don’t have the workers, either. So you’re going to be sitting there tapping away one brick at a time and hoping that maybe your grandson or your granddaughter might complete the project.”

Deep-seated issues

The movement to provide those blueprints is known as language justice, and it is not new. The American Bar Association describes language justice as a “framework” that preserves people’s rights “to communicate, understand, and be understood in the language in which they prefer and feel most articulate and powerful.” 


Transforming health care at the edge



Transforming health care at the edge

Edge computing, through on-site sensors and devices, as well as last-mile edge equipment that connects to those devices, allows data processing and analysis to happen close to the digital interaction. Rather than using centralized cloud or on-premises infrastructure, these distributed tools at the edge offer the same quality of data processing but without latency issues or massive bandwidth use.

“The real-time feedback loop required for things like remote monitoring of a patient’s heart and respiratory metrics is only possible with something like edge computing,” Mirchandani says. “If all that information took several seconds or a minute to get processed somewhere else, it’s useless.”

Opportunities and challenges at the health-care edge

The sky’s the limit when it comes to the opportunities to use edge computing in health care, says Paul Savill, senior vice president of product management and services at technology company Lumen, especially as health systems work to reduce costs by shifting testing and treatment out of hospitals and into clinics, retail locations, and homes.

“A lot of patient care now happens at retail drugstores, whether it is blood work, scans, or other assessments,” Savill says. “With edge computing capabilities and tools, that can now take place on-site, on a real-time basis, so you don’t have to send things to a lab and wait a day or week to get results back.”

The arrival of 5G technology, the new standard for broadband cellular networks, will also drive opportunities, as it works with edge computing tools to support the internet of things and machine learning, adds Mirchandani. “It’s the combination of this super-low-latency network and computing at the edge that will help these powerful new applications take flight,” he says. Take robotic surgeries—it’s crucial for the surgeon to have nearly instant, sub-millisecond sensory feedback. “That’s not possible in any other way than through technologies such as edge computing and 5G,” he says.

“A lot of patient care now happens at retail drugstores. With edge computing capabilities and tools, that can now take place on-site, on a real-time basis, so you don’t have to send things to a lab and wait a day or week to get results back.”

Paul Savill, Senior Vice President, Product Management and Services, Lumen

Data security, however, is a particular challenge for any health-care-related technology because of HIPAA, the US health information privacy law, and other regulations. The real-time data transmission edge computing provides will be under significant scrutiny, Mirchandani explains, which may affect widespread adoption. “There needs to be an almost 100% guarantee that the information you generate from a heart monitor, pulse oximeter, blood glucose monitor, or any other device will not be intercepted or disrupted in any way,” he says.

Still, edge computing technologies, paired with the right security standards and tools, are often more secure and reliable than the on-premises environment a business could implement on its own, Savill points out. “It’s about understanding the entire threat landscape down to the network level.”

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Anti-vaxxers are weaponizing Yelp to punish bars that require vaccine proof



Anti-vaxxers are weaponizing Yelp to punish bars that require vaccine proof

Smith’s Yelp reviews were shut down after the sudden flurry of activity on its page, which the company labels “unusual activity alerts,” a stopgap measure for both the business and Yelp to filter through a flood of reviews and pick out which are spam and which aren’t. Noorie Malik, Yelp’s vice president of user operations, said Yelp has a “team of moderators” that investigate pages that get an unusual amount of traffic. “After we’ve seen activity dramatically decrease or stop, we will then clean up the page so that only firsthand consumer experiences are reflected,” she said in a statement.

It’s a practice that Yelp has had to deploy more often over the course of the pandemic: According to Yelp’s 2020 Trust & Safety Report, the company saw a 206% increase over 2019 levels in unusual activity alerts. “Since January 2021, we’ve placed more than 15 unusual activity alerts on business pages related to a business’s stance on covid-19 vaccinations,” said Malik.

The majority of those cases have been since May, like the gay bar C.C. Attles in Seattle, which got an alert from Yelp after it made patrons show proof of vaccination at the door. Earlier this month, Moe’s Cantina in Chicago’s River North neighborhood got spammed after it attempted to isolate vaccinated customers from unvaccinated ones.

Spamming a business with one-star reviews is not a new tactic. In fact, perhaps the best-known case is Colorado’s Masterpiece bakery, which won a 2018 Supreme Court battle for refusing to make a wedding cake for a same-sex couple, after which it got pummeled by one-star reviews. “People are still writing fake reviews. People will always write fake reviews,” Liu says.

But he adds that today’s online audience know that platforms use algorithms to detect and flag problematic words, so bad actors can mask their grievances by blaming poor restaurant service like a more typical negative review to ensure the rating stays up — and counts.

That seems to have been the case with Knapp’s bar. His Yelp review included comments like “There was hair in my food” or alleged cockroach sightings. “Really ridiculous, fantastic shit,” Knapp says. “If you looked at previous reviews, you would understand immediately that this doesn’t make sense.” 

Liu also says there is a limit to how much Yelp can improve their spam detection, since natural language — or the way we speak, read, and write — “is very tough for computer systems to detect.” 

But Liu doesn’t think putting a human being in charge of figuring out which reviews are spam or not will solve the problem. “Human beings can’t do it,” he says. “Some people might get it right, some people might get it wrong. I have fake reviews on my webpage and even I can’t tell which are real or not.”

You might notice that I’ve only mentioned Yelp reviews thus far, despite the fact that Google reviews — which appear in the business description box on the right side of the Google search results page under “reviews” — is arguably more influential. That’s because Google’s review operations are, frankly, even more mysterious. 

While businesses I spoke to said Yelp worked with them on identifying spam reviews, none of them had any luck with contacting Google’s team. “You would think Google would say, ‘Something is fucked up here,’” Knapp says. “These are IP addresses from overseas. It really undermines the review platform when things like this are allowed to happen.”

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These creepy fake humans herald a new age in AI



Once viewed as less desirable than real data, synthetic data is now seen by some as a panacea. Real data is messy and riddled with bias. New data privacy regulations make it hard to collect. By contrast, synthetic data is pristine and can be used to build more diverse data sets. You can produce perfectly labeled faces, say, of different ages, shapes, and ethnicities to build a face-detection system that works across populations.

But synthetic data has its limitations. If it fails to reflect reality, it could end up producing even worse AI than messy, biased real-world data—or it could simply inherit the same problems. “What I don’t want to do is give the thumbs up to this paradigm and say, ‘Oh, this will solve so many problems,’” says Cathy O’Neil, a data scientist and founder of the algorithmic auditing firm ORCAA. “Because it will also ignore a lot of things.”

Realistic, not real

Deep learning has always been about data. But in the last few years, the AI community has learned that good data is more important than big data. Even small amounts of the right, cleanly labeled data can do more to improve an AI system’s performance than 10 times the amount of uncurated data, or even a more advanced algorithm.

That changes the way companies should approach developing their AI models, says Datagen’s CEO and cofounder, Ofir Chakon. Today, they start by acquiring as much data as possible and then tweak and tune their algorithms for better performance. Instead, they should be doing the opposite: use the same algorithm while improving on the composition of their data.

Datagen also generates fake furniture and indoor environments to put its fake humans in context.


But collecting real-world data to perform this kind of iterative experimentation is too costly and time intensive. This is where Datagen comes in. With a synthetic data generator, teams can create and test dozens of new data sets a day to identify which one maximizes a model’s performance.

To ensure the realism of its data, Datagen gives its vendors detailed instructions on how many individuals to scan in each age bracket, BMI range, and ethnicity, as well as a set list of actions for them to perform, like walking around a room or drinking a soda. The vendors send back both high-fidelity static images and motion-capture data of those actions. Datagen’s algorithms then expand this data into hundreds of thousands of combinations. The synthesized data is sometimes then checked again. Fake faces are plotted against real faces, for example, to see if they seem realistic.

Datagen is now generating facial expressions to monitor driver alertness in smart cars, body motions to track customers in cashier-free stores, and irises and hand motions to improve the eye- and hand-tracking capabilities of VR headsets. The company says its data has already been used to develop computer-vision systems serving tens of millions of users.

It’s not just synthetic humans that are being mass-manufactured. Click-Ins is a startup that uses synthetic AI to perform automated vehicle inspections. Using design software, it re-creates all car makes and models that its AI needs to recognize and then renders them with different colors, damages, and deformations under different lighting conditions, against different backgrounds. This lets the company update its AI when automakers put out new models, and helps it avoid data privacy violations in countries where license plates are considered private information and thus cannot be present in photos used to train AI.

Click-Ins renders cars of different makes and models against various backgrounds.

CLICK-INS works with financial, telecommunications, and insurance companies to provide spreadsheets of fake client data that let companies share their customer database with outside vendors in a legally compliant way. Anonymization can reduce a data set’s richness yet still fail to adequately protect people’s privacy. But synthetic data can be used to generate detailed fake data sets that share the same statistical properties as a company’s real data. It can also be used to simulate data that the company doesn’t yet have, including a more diverse client population or scenarios like fraudulent activity.

Proponents of synthetic data say that it can help evaluate AI as well. In a recent paper published at an AI conference, Suchi Saria, an associate professor of machine learning and health care at Johns Hopkins University, and her coauthors demonstrated how data-generation techniques could be used to extrapolate different patient populations from a single set of data. This could be useful if, for example, a company only had data from New York City’s more youthful population but wanted to understand how its AI performs on an aging population with higher prevalence of diabetes. She’s now starting her own company, Bayesian Health, which will use this technique to help test medical AI systems.

The limits of faking it

But is synthetic data overhyped?

When it comes to privacy, “just because the data is ‘synthetic’ and does not directly correspond to real user data does not mean that it does not encode sensitive information about real people,” says Aaron Roth, a professor of computer and information science at the University of Pennsylvania. Some data generation techniques have been shown to closely reproduce images or text found in the training data, for example, while others are vulnerable to attacks that make them fully regurgitate that data.

This might be fine for a firm like Datagen, whose synthetic data isn’t meant to conceal the identity of the individuals who consented to be scanned. But it would be bad news for companies that offer their solution as a way to protect sensitive financial or patient information.

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