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Why then attack on the Capitol was inevitable

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Why then attack on the Capitol was inevitable


Maybe you saw this coming nearly a decade ago, when #YourSlipIsShowing laid bare how racist Twitter users were impersonating Black women on the internet. Maybe, for you, it was during Gamergate, the online abuse campaign targeting women in the industry. Or maybe it was the mass shooting in Christchurch, when a gunman steeped in the culture of 8chan livestreamed himself murdering dozens of people. 

Maybe it was when you, or your friend, or your community, became the target of an extremist online mob, and you saw online anger become real world danger and harm. 

Or maybe what happened on Wednesday, when a rabble of internet-fuelled Trump supporters invaded the Capitol, came as a surprise.

For weeks they had been planning their action in plain sight on the internet—but they have been showing you who they are for years. The level of shock you feel right now about the power and danger of online extremism depends on whether you were paying attention. 

The consequences of inaction

The mob who tried to block Congress from confirming Joe Biden’s presidential victory  showed how the stupidity and danger of the far-right internet could come into the real world again, but this time it struck at the center of the US government. Neo-nazi streamers weren’t just inside the Capitol, they were putting on a show for audiences of tens of thousands of people who egged them on in the chats. The mob was having fun doing memes in the halls of American democracy as a woman—a Trump supporter whose social media history shows her devotion to QAnon—was killed trying to break into Congressional offices.

The past year, especially since the pandemic, has been one giant demonstration of the consequences of inaction; the consequences of ignoring the many, many people who have been begging social media companies to take the meme-making extremists and conspiracy theorists that have thrived on their platforms seriously. 

Facebook and Twitter acted to slow the rise of QAnon over the summer, but only after the pro-Trump conspiracy theory was able to grow relatively unrestricted there for three years. Account bans and algorithm tweaks have long been too little, too late to deal with racists, extremists and conspiracy theorists, and they have rarely addressed the fact that these powerful systems were working exactly as intended.    

I spoke with a small handful of the people who could have told you this was coming about this for a story in October. Researchers, technologists, and activists told me that major social media companies have, for the entirety of their history, chosen to do nothing, or to act only after their platforms cause abuse and harm. 

Ariel Waldman tried to get Twitter to meaningfully address abuse there in 2008. Researchers like Shafiqah Hudson, I’Nasah Crockett, and Shireen Mitchell have tracked exactly how harassment works and finds an audience on these platforms for years. Whitney Phillips talked about how she’s haunted by laughter—not just from other people, but also her own—back in the earliest days of her research into online culture and trolling, when overwhelmingly white researchers and personalities treated the extremists among them as edgy curiosities.

Many, many people who have been begging social media companies to take the meme-making extremists and conspiracy theorists seriously.

Ellen Pao, who briefly served as CEO of Reddit in 2014 and stepped down after introducing the platform’s first anti-harassment policy, was astonished that Reddit had only banned r/The_Donald in June 2020, after evidence had built for years to show that the popular pro-Trump message board served as an organizing space for extremiss and a channel for mob abuse. Of course, by the time it was banned, many of its users had already migrated away from Reddit to TheDonald.win, an independent forum created by the same people who ran the previous version. Its pages were filled with dozens of calls for violence ahead of Wednesday’s rally-turned-attempted-coup. 

Banning Trump doesn’t solve the issue

Facebook, Twitter, and YouTube didn’t create conspiracy thinking, or extremist ideologies, of course. Nor did they invent the idea of dangerous personality cults. But these platforms have—by design—handed those groups the mechanisms to reach much larger audiences much faster, and to recruit and radicalize new converts, even at the expense of the people and communities those ideologies target for abuse. And crucially, even when it was clear what was happening, they chose the minimal amount of change—or decided not to intervene at all. 

In the wake of the attempted coup on the Capitol building, people are again looking at the major social media companies to see how they respond. The focus is on Trump’s personal accounts, which he used to encourage supporters to descend on DC and then praised them when they did. Will he be banned from Twitter? There are compelling arguments for why he should. 

But as heavy and consequential as that would be, it’s also, in other ways… not. Abuse, harassment, conspiracy thinking, and racism will still be able to benefit from social media companies that remain interested in only acting when it’s too late, even without Trump retweeting them and egging them on. 

Facebook has banned Trump indefinitely, and also increased the extent of their moderation of groups, where a lot of conspiracy-fueled activity lives. These changes are good, but again, not new: people have told Facebook about this for years; Facebook employees have told Facebook about this for years. Groups were instrumental in organizing Stop the Steal protests in the days after the election, and before that, in anti-mask protests, and before that in spreading fake news, and before that in as a central space for anti-vaccine misinformation. None of this is new. 

There are only so many ways to say that more people should have listened. If you’re paying attention now, maybe you’ll finally start hearing what they say. 

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Banks need to strike the right balance for digital transformation

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Banks need to strike the right balance for digital transformation


Every financial institution is looking to digital transformation to meet rising customer expectations for speed and convenience, lower its operating cost, and fend off competition, including from tech companies moving into financial services. Some are spending over 10% of yearly revenue on technology investments, according to Bloomberg. “This is a huge investment and most financial institutions cannot support this for the long term,” says Michael Fei, SME banking CEO at OneConnect Financial Technology, an associate of Ping An Insurance.

The covid-19 pandemic has revealed how even financial institutions that considered themselves digitally advanced are, in reality, still wedded to analog processes along the chain of processing.

“For many financial institutions, this has been a wake-up call,” says Fei. “In the past, many had thought that if they have an online portal and a mobile application then that’s enough. But now they’ve realized it’s not. Some banks have online portals and mobile apps where you can apply for loans, but they still need to send items to the customer and carry out on-site inspection before they can process the loans, which hasn’t been possible during covid. Banks have had to reshape and redesign the whole process of their lending products.”

Banks have also realized their lack of truly deep customer knowledge, which is crucial to inform responsible and flexible decisions during an economic downturn as customer needs rapidly change.  

“Now that everything is digital, financial institutions are realizing how little they knew their customers,” says Tan Bin Ru, chief executive officer for Southeast Asia at OneConnect Financial Technology. “Customer hyper-personalization tools, to understand what products to offer, have been acknowledged conceptually for a long time but not implemented—now banks are moving towards it and really getting tools to do it.” Traditional banks that were not previously utilizing alternative datasets now want to integrate them more into secure lending, Tan says.

The power of partnerships

Banks have increasingly understood they need outside help to execute their digital transformation agenda. “Banks usually have very rigid systems and procedures,” says Fei. “For instance, if you want to launch a new product you have to follow the process, and it takes at least six months. In the age of digitalization, this doesn’t work, as customers want things immediately. This has put huge pressure on these financial institutions to build agile operations and systems to be able to respond to the needs of their customers.”

But the number of tech companies pushing into financial services can be overwhelming and not all of them have domain expertise, which can lead to misguided attempts to apply new technologies everywhere. Without experience of financial services, tech companies may also underestimate the trade-offs involved in deploying certain digital tools. 

OneConnect combines expertise in digital technology with deep knowledge of banking. Fei, who has past experience working at HSBC China and Bank of Langfang, a Chinese commercial bank, describes one partnership with a Chinese national bank to reimagine its customer service center as an illustration of why banking experience matters in digital reform. The lender was looking to transform a 6,000-person call center toward a more intelligent, AI-enabled approach with greater use of automation. But automating customer services must be done carefully; customers will not appreciate being handed off to a robot for certain sensitive or urgent inquiries where a human counterpart is desired.  

OneConnect built a knowledge map with the bank, to understand and anticipate what problem a customer is trying to solve with a given query, and then understanding when and where to apply automation versus human support. “This required extensive understanding of the business and the industry, which many technology companies do not have,” he says. “You need that, to know when to intervene, what should be done by robotics and what should be a human being. Many tech companies cannot offer this.”

Rather than advocating digital transformation across the board, OneConnect works to get the right balance between customization and integration, and to appreciate that banks are looking for a blend, or omnichannel approach. “Our banking customers, and their customers, want to be offline for certain things, and online for others; they want that flexibility,” says Tan.

A second partnership problem banks face is the sheer number of technology vendors and startups, which can be overwhelming and complicate their digital transformation journey. It is unclear which fintechs will survive and which will not; startups might offer an appealing technology, but if their underlying business model proves unviable, or they cannot raise sufficient funding to support their expansion, or they pivot to a new direction, a bank is exposed.

In many cases, banks take on many different fintechs because no single startup can manage the breadth of their needs, or because the bank wants to diversify its risk. “Since the digital journey is such a long process, a lot of banks feel they need to look at 15 to 20 fintechs to piece together their journey, but the more players they have, the more risk there is,” says Tan.

OneConnect solves both problemsan overly complicated vendor network and the risk of working with fledgling tech companies—by offering a broad sweep of turnkey solutions, with the commercial scale and security that customers can rely on. Typically, a bank will chart its desired journey and up to 80% of those solutions can be provided by OneConnect, says Tan. The company, publicly traded on the New York Stock Exchange, also draws on over 30 years of experience in financial services of its parent company, Ping An, described by The Economist as a window into the future of finance. “No other traditional financial-services group in the world comes close to rivaling Ping An’s ability to develop technologies and deploy them at such a scale,” the magazine recently wrote.

OneConnect: The journey so far

OneConnect has built a broad business in China, serving all of its major banks, 99% of its city commercial banks, and 53% of insurance companies. But its footprint is increasingly global, with over 50 international customers in more than 15 markets, including Singapore, Indonesia, Malaysia, Philippines, and Abu Dhabi.

The company has built new technology solutions to enhance pricing accuracy, such as an alternative data, AI-based credit scoring model for a credit bureau in Indonesia, and supported Malaysian banks to develop user-friendly apps, digital portals, and onboarding. It is leveraging image recognition, a core enabler of “insur-tech” that allows insurers to quickly assess damage claims and pay out to eligible beneficiaries. OneConnect has partnered with Swiss Re, a European insurer, to develop a digital end-to-end solution for motor claims handling, based on AI-based image recognition and advanced data analytics. The tool can analyze photos of vehicle damage, identify repair needs and costs within minutes, offer cash payments, and even offer value-added services, like directing drivers to a repair garage.

OneConnect is also helping build the fintech ecosystem by working with governments, regulators, and stakeholders. It is working with Singapore’s blockchain association to build the skills, literacy, and talent pool needed to enable innovation and has partnered with Abu Dhabi Global Market, a financial center in the United Arab Emirates, to support the development of a “digital lab,” a sandbox for fintechs to collaborate and develop their innovations.   

Working closely with its partners at home and abroad, OneConnect is helping the finance industry move swiftly into the digital era by leveraging the right tools at the right time, benefiting customers and finance institutions alike by widening access to services and lowering costs.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

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Will your neighbors get vaccinated?

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Will your neighbors get vaccinated?


As the coronavirus vaccines have rolled out across the US, the process has been confusing and disastrous. States, left by the federal government to fend for themselves, have struggled to get a handle on the logistics of distribution. Many, including Georgia, Virginia, and California, have fallen woefully behind schedule.

But even if there were a perfect supply chain, there’s another obstacle: Not all Americans want the vaccine.

Survey data gathered through Facebook by Carnegie Mellon University’s Delphi Lab, one of the nation’s best flu-forecasting teams, showed that more than a quarter of the country’s population would not get vaccinated if it were available to them today. How people feel about receiving vaccinations varies widely by state and county. The percentage of respondents who would accept a vaccine falls as low as 48% in Terrebonne parish, Louisiana, and peaks as high as 92% in Arlington county, Virginia.

The findings are extremely worrying. The fewer people who are vaccinated, the longer the virus will continue to ravage the country, and prevent us from returning to normal. “It’s one of those things that probably shouldn’t have surprised me,” says Alex Reinhart, an assistant teaching professor in statistics & data science, who was part of the research. “But when you look at the map, it’s still surprising to see.”

The good news—and there is some good news—is that this data could also help fight public hesitancy. The Delphi Lab has been helping the CDC to track and understand the spread of covid infections since the beginning of the pandemic. The latest survey will help the agency identify where to perform more targeted education campaigns. The research group is also working with several county-level health departments to inform local outreach.

The Delphi researchers collected the data via a large-scale survey that it has been operating through Facebook since April 2019. It works with the social media giant to reach as wide a cross-section of the US population as possible, and surfaces daily questions to a statistically representative sample of Facebook users. An average of 56,000 people participate daily, and the company itself never sees the results.

During the pandemic, the survey has included a variety of questions to understand people’s covid-related behaviors, including mask adherence, social distancing, and their mental health. Some of the results are fed into the lab’s coronavirus forecasting model, while others are summarized and given directly to public health officials and other academic researchers. The questions are regularly updated, and the vaccine acceptance question was added at the start of January—after the first vaccines had been authorized by the US government.

The map visualizes each county’s polling average from January 1 to January 14. For counties with too few daily respondents—less than 100—the Delphi researchers grouped the data from neighboring counties. This is reflected in our map above, which is why various clusters of counties show up with the same percentage. The researchers also independently verified their results with some of the CDC’s own surveys and Pew Research.

Next, the researchers plan to expand their survey to understand why people are reticent about the vaccine. They’re also exploring questions that could help identify what blocks people from accessing vaccines, especially for at-risk populations.

This story is part of the Pandemic Technology Project, supported by the Rockefeller Foundation.

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Worried about your firm’s AI ethics? These startups are here to help.

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A screenshot of Parity's library of impact assessment questions.


Parity is among a growing crop of startups promising organizations ways to develop, monitor, and fix their AI models. They offer a range of products and services from bias-mitigation tools to explainability platforms. Initially most of their clients came from heavily regulated industries like finance and health care. But increased research and media attention on issues of bias, privacy, and transparency have shifted the focus of the conversation. New clients are often simply worried about being responsible, while others want to “future proof” themselves in anticipation of regulation.

“So many companies are really facing this for the first time,” Chowdhury says. “Almost all of them are actually asking for some help.”

From risk to impact

When working with new clients, Chowdhury avoids using the term “responsibility.” The word is too squishy and ill-defined; it leaves too much room for miscommunication. She instead begins with more familiar corporate lingo: the idea of risk. Many companies have risk and compliance arms, and established processes for risk mitigation.

AI risk mitigation is no different. A company should start by considering the different things it worries about. These can include legal risk, the possibility of breaking the law; organizational risk, the possibility of losing employees; or reputational risk, the possibility of suffering a PR disaster. From there, it can work backwards to decide how to audit its AI systems. A finance company, operating under the fair lending laws in the US, would want to check its lending models for bias to mitigate legal risk. A telehealth company, whose systems train on sensitive medical data, might perform privacy audits to mitigate reputational risk.

Parity includes a library of suggested questions to help companies evaluate the risk of their AI models.

PARITY

Parity helps to organize this process. The platform first asks a company to build an internal impact assessment—in essence, a set of open-ended survey questions about how its business and AI systems operate. It can choose to write custom questions or select them from Parity’s library, which has more than 1,000 prompts adapted from AI ethics guidelines and relevant legislation from around the world. Once the assessment is built, employees across the company are encouraged to fill it out based on their job function and knowledge. The platform then runs their free-text responses through a natural-language processing model and translates them with an eye toward the company’s key areas of risk. Parity, in other words, serves as the new go-between in getting data scientists and lawyers on the same page.

Next, the platform recommends a corresponding set of risk mitigation actions. These could include creating a dashboard to continuously monitor a model’s accuracy, or implementing new documentation procedures to track how a model was trained and fine-tuned at each stage of its development. It also offers a collection of open-source frameworks and tools that might help, like IBM’s AI Fairness 360 for bias monitoring or Google’s Model Cards for documentation.

Chowdhury hopes that if companies can reduce the time it takes to audit their models, they will become more disciplined about doing it regularly and often. Over time, she hopes, this could also open them to thinking beyond risk mitigation. “My sneaky goal is actually to get more companies thinking about impact and not just risk,” she says. “Risk is the language people understand today, and it’s a very valuable language, but risk is often reactive and responsive. Impact is more proactive, and that’s actually the better way to frame what it is that we should be doing.”

A responsibility ecosystem

While Parity focuses on risk management, another startup, Fiddler, focuses on explainability. CEO Krishna Gade began thinking about the need for more transparency in how AI models make decisions while serving as the engineering manager of Facebook’s News Feed team. After the 2016 presidential election, the company made a big internal push to better understand how its algorithms were ranking content. Gade’s team developed an internal tool that later became the basis of the “Why am I seeing this?” feature.

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