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Rebalancing the data economy: Startups for a restart

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3 years ago

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Rebalancing the data economy: Startups for a restart


The big data era has created valuable resources for public interest outcomes, like health care. In the last 18 months, the speed with which scientists were able to respond to the covid-19 pandemic—faster than any other disease in history—demonstrated the benefits of gathering, sharing, and extracting value from data for a wider good.

Access to data from 56 million National Health Service (NHS) patients’ medical records enabled public health researchers in the UK to provide some of the strongest data on risk factors for covid mortality and features of long covid, while access to sensitive health records sped up the development of lifesaving medical treatments like the messenger-RNA vaccines produced by Moderna and Pfizer.

But balancing the benefits of data sharing with the protection of individual and organizational privacy is a delicate process—and rightly so. Governments and businesses are increasingly collecting vast amounts of data, prompting investigations, concerns around privacy, and calls for stricter regulation.

“Data increasingly powers innovation, and it needs to be used for the public good, while individual privacy is protected. This is new and unfamiliar terrain for policymaking, and it requires a careful approach,” wrote David Deming, professor and director of the Malcolm Wiener Center for Social Policy at the Harvard Kennedy School, in a recent New York Times article.

A growing number of startups—some 230 and counting, according to Data Collaboratives—are forming to help empower citizens, nonprofit groups, and governments to gain more control over their data.

These startups are adopting legal and institutional structures like data trusts, cooperatives, and stewards to help provide people and organizations with a means of effectively gathering and using relevant data—and in the process, taking on Big Tech’s control of the data economy.

“The relationship between data and society is fundamentally broken,” says Matt Gee, CEO of Brighthive, which helps networks and organizations set up alternative governance models including data trusts, data commons, and data cooperatives.

“We think it should be more collaborative instead of competitive, it should be more open and transparent, it should be more distributed and democratic instead of monopolistic. This is how we make the gains more equitable and reduce harmful biases in data.” 

Access and control

As demonstrated by the pandemic, medical research and public health planning can be enriched by access to electronic health records, prescription and medicines data, and epidemiology. But health data are also highly sensitive, with understandable public scrutiny over efforts to share them. So-called “secondary use,” which applies personal health information for uses outside health-care delivery, requires a new governance framework.

Findata is an independent authority in the Finnish Institute of Health and Welfare, established by a government act in May 2019. The agency facilitates researchers’ access to Finnish health data, issuing permits for use or responding to specific statistical requests. In so doing, it aims to protect the interests of citizens while also appreciating the value that their data could offer to medical research, teaching, and health planning.

Prior to the formation of Findata, it was costly and complex for researchers to access this vital research resource. “The purpose of this agency is to streamline and secure the use of health data,” explains Johanna Seppänen, director of Findata.

“Before, if you wanted to have data from different registers or hospitals, you had to make four applications, and there were no standard ways of handling them, no ways to determine prices. It was very time-consuming, difficult, and confusing.”

Findata is the only agency of its kind so far, but it might inspire other countries that want to realize more value from health data in a safe and secure way.

The UK’s NHS recently faced pushback from privacy campaigners over reforms to improve data sharing for public health planning, showing the challenges that can come from attempts to change data collection and sharing protocols. 

Empowerment and autonomy

Helping disenfranchised individuals and groups has been another focus area for new data governance organizations.

Data stewards—which range from community-based collectives to public or private organizations—serve as “both intermediaries and guardians during the exchange of data, thereby supporting individuals and communities to better navigate the data economy and better negotiate on their data rights,” says Suha Mohamed, strategy and partnerships associate at Aapti, an organization working on the intersection of technology and society with a focus on data rights. 

One example of where data stewards can prove useful is for individuals in the gig economy, a fast-growing labor market that has been characterized by the prevalence of short-term contracts or freelance work, as opposed to permanent jobs, and has been rife with power inequalities.  

“Asymmetric control of data is one of the primary levers of power that gig platforms use to manage their workforce and shape the narrative and public policy in the arena that they operate in,” says Hays Witt, co-founder and CEO of Driver’s Seat, a driver-owned data cooperative specializing in ride-hailing.

“Very few stakeholders have access to the data they need to engage in productive and constructive ways, starting with gig workers themselves. Our premise [at Driver’s Seat] is: let’s use tech and a data cooperative to empower gig workers to collect, aggregate, and share their data,” says Witt.

Driver’s Seat has developed a proprietary app through which workers can submit their location, work, and earnings information, which is then aggregated and analyzed. Drivers then receive insights that help them understand their real earnings and performance, informing their choices about where, when, on what platforms, and on what terms to work.

Driver’s Seat is developing tools that can tell drivers their average real pay across platforms in their city, compare their pay with averages, and tell them whether their pay is going up or down. All of this could help drivers move to platforms that offer them a better deal, empowering what is an otherwise atomized labor force.

“Our drivers are really excited to be engaged, because their day-to-day experience is seeing metrics, fed back to them by the platforms, that they don’t trust,” says Witt. “They know that the metrics are influential, their day-to-day experience is totally mediated by data. It impacts their earnings and their life, and they know it.”

Witt believes that in the future, workers will increasingly be able to contribute to crowdsourced information to develop “collective analyses of their problems, which means they can put forward collective policy solutions or agreements to negotiate with the employment platform.”

Balancing social mission and business models

All data startups, whether they are government-sanctioned institutions like Findata or entrepreneurial businesses like Driver’s Seat, face the challenge of balancing their mission with operational sustainability.

Securing a sustainable financial footing is a major challenge for nonprofit groups and social impact businesses. For data equity institutions, the funding mix commonly includes community- and membership-driven approaches, and philanthropic aid.

But some organizations, like Brighthive, have found win-win models where private sector companies are looking to improve data governance and are willing to pay for it.

Brighthive’s Gee describes commercial clients who have “seen what’s happening in the European Union around AI regulation and they want to get ahead of it in the US. They are taking a proactive stance on issues like algorithmic transparency, equity audits, and an alternative governance model for how they use customer data.”

Other data equity platforms have found revenue models in which beneficiary data can be harnessed by third parties in socially positive ways. Hays Witt at Driver’s Seat cites the example of municipal authorities and planning agencies.

Both the authorities and ride-hailing drivers have an incentive to reduce “dead time” in which a driver is circulating without earning money, causing emissions and congestion. If appropriate data can be collected, aggregated, and analyzed in a useful way, it can lead to better traffic and mobility decisions and infrastructure interventions. So, all participants benefit.

Witt points out other “neutral” cases where beneficiary data could be valuable to unrelated private sector entities in ways that do not work against the interests of the drivers. He gives the example of commercial real estate developers who are often forced to make decisions about investments and services based on out-of-date traffic and mobility data.

Driver’s Seat is exploring opportunities to offer aggregated analytics products to such companies with revenues returned as dividends to gig workers and to help finance the cooperative.

Many data startups seeking out sustainable revenue opportunities need to decide where to draw the line in terms of the kind of work they are willing to take on or the kind of businesses they’re willing to work with.

Brighthive’s Matt Gee points to growing investor interest in startups that can help companies navigate the end of “cookies,” which have been critical to third-party advertising but are now being phased out. “Investors are concerned about the death of third-party data and are hungry for companies addressing that,” he says.

But as socially minded startups gain more business from corporate clients, they need to balance their mission for social good with the financial gain of lucrative contracts.

“Is being a public benefit corporation more about what you do and how you do it, or who you work with? If we work on a data collaborative that provides transparency and accountability for marketing organizations pooling customer lists, are we actually reducing societal harm? These are questions that our team is constantly grappling with,” says Gee.

Data startups will inevitably face challenges, including balancing social mission, ethics, and business models, but as the data economy continues to grow, they are in a unique position to carve out new ways of responsibly leveraging the insight that data can provide for citizens, organizations, and governments—wresting some of the power over data away from Big Tech.

“Our data economy needs to anchor on creating value for everyone in society, and that requires user control, trusted intermediation, and collective governance to be embedded in innovative data stewardship models,” says Sushant Kumar, principal of responsible technology at social change venture Omidyar Network.

“Onboarding a critical mass of users, receiving regulatory support, and achieving financial sustainability will also ensure these designs succeed in disrupting the status quo and injecting fairness into the current paradigm.”

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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Why I became a TechTrekker

Published

1 year ago

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22 August 2023

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group jumps into the air with snowy mountains in the background


My senior spring in high school, I decided to defer my MIT enrollment by a year. I had always planned to take a gap year, but after receiving the silver tube in the mail and seeing all my college-bound friends plan out their classes and dorm decor, I got cold feet. Every time I mentioned my plans, I was met with questions like “But what about school?” and “MIT is cool with this?”

Yeah. MIT totally is. Postponing your MIT start date is as simple as clicking a checkbox. 

Sofia Pronina (right) was among those who hiked to the Katla Glacier during this year’s TechTrek to Iceland.

COURTESY PHOTO

Now, having finished my first year of classes, I’m really grateful that I stuck with my decision to delay MIT, as I realized that having a full year of unstructured time is a gift. I could let my creative juices run. Pick up hobbies for fun. Do cool things like work at an AI startup and teach myself how to create latte art. My favorite part of the year, however, was backpacking across Europe. I traveled through Austria, Slovakia, Russia, Spain, France, the UK, Greece, Italy, Germany, Poland, Romania, and Hungary. 

Moreover, despite my fear that I’d be losing a valuable year, traveling turned out to be the most productive thing I could have done with my time. I got to explore different cultures, meet new people from all over the world, and gain unique perspectives that I couldn’t have gotten otherwise. My travels throughout Europe allowed me to leave my comfort zone and expand my understanding of the greater human experience. 

“In Iceland there’s less focus on hustle culture, and this relaxed approach to work-life balance ends up fostering creativity. This was a wild revelation to a bunch of MIT students.”

When I became a full-time student last fall, I realized that StartLabs, the premier undergraduate entrepreneurship club on campus, gives MIT undergrads a similar opportunity to expand their horizons and experience new things. I immediately signed up. At StartLabs, we host fireside chats and ideathons throughout the year. But our flagship event is our annual TechTrek over spring break. In previous years, StartLabs has gone on TechTrek trips to Germany, Switzerland, and Israel. On these fully funded trips, StartLabs members have visited and collaborated with industry leaders, incubators, startups, and academic institutions. They take these treks both to connect with the global startup sphere and to build closer relationships within the club itself.

Most important, however, the process of organizing the TechTrek is itself an expedited introduction to entrepreneurship. The trip is entirely planned by StartLabs members; we figure out travel logistics, find sponsors, and then discover ways to optimize our funding. 

two students soaking in a hot spring in Iceland

COURTESY PHOTO

In organizing this year’s trip to Iceland, we had to learn how to delegate roles to all the planners and how to maintain morale when making this trip a reality seemed to be an impossible task. We woke up extra early to take 6 a.m. calls with Icelandic founders and sponsors. We came up with options for different levels of sponsorship, used pattern recognition to deduce the email addresses of hundreds of potential contacts at organizations we wanted to visit, and all got scrappy with utilizing our LinkedIn connections.

And as any good entrepreneur must, we had to learn how to be lean and maximize our resources. To stretch our food budget, we planned all our incubator and company visits around lunchtime in hopes of getting fed, played human Tetris as we fit 16 people into a six-person Airbnb, and emailed grocery stores to get their nearly expired foods for a discount. We even made a deal with the local bus company to give us free tickets in exchange for a story post on our Instagram account. 

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The Download: spying keyboard software, and why boring AI is best

Published

1 year ago

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22 August 2023

By

🧠


This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How ubiquitous keyboard software puts hundreds of millions of Chinese users at risk

For millions of Chinese people, the first software they download onto devices is always the same: a keyboard app. Yet few of them are aware that it may make everything they type vulnerable to spying eyes. 

QWERTY keyboards are inefficient as many Chinese characters share the same latinized spelling. As a result, many switch to smart, localized keyboard apps to save time and frustration. Today, over 800 million Chinese people use third-party keyboard apps on their PCs, laptops, and mobile phones. 

But a recent report by the Citizen Lab, a University of Toronto–affiliated research group, revealed that Sogou, one of the most popular Chinese keyboard apps, had a massive security loophole. Read the full story. 

—Zeyi Yang

Why we should all be rooting for boring AI

Earlier this month, the US Department of Defense announced it is setting up a Generative AI Task Force, aimed at “analyzing and integrating” AI tools such as large language models across the department. It hopes they could improve intelligence and operational planning. 

But those might not be the right use cases, writes our senior AI reporter Melissa Heikkila. Generative AI tools, such as language models, are glitchy and unpredictable, and they make things up. They also have massive security vulnerabilities, privacy problems, and deeply ingrained biases. 

Applying these technologies in high-stakes settings could lead to deadly accidents where it’s unclear who or what should be held responsible, or even why the problem occurred. The DoD’s best bet is to apply generative AI to more mundane things like Excel, email, or word processing. Read the full story. 

This story is from The Algorithm, Melissa’s weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.

The ice cores that will let us look 1.5 million years into the past

To better understand the role atmospheric carbon dioxide plays in Earth’s climate cycles, scientists have long turned to ice cores drilled in Antarctica, where snow layers accumulate and compact over hundreds of thousands of years, trapping samples of ancient air in a lattice of bubbles that serve as tiny time capsules. 

By analyzing those cores, scientists can connect greenhouse-gas concentrations with temperatures going back 800,000 years. Now, a new European-led initiative hopes to eventually retrieve the oldest core yet, dating back 1.5 million years. But that impressive feat is still only the first step. Once they’ve done that, they’ll have to figure out how they’re going to extract the air from the ice. Read the full story.

—Christian Elliott

This story is from the latest edition of our print magazine, set to go live tomorrow. Subscribe today for as low as $8/month to ensure you receive full access to the new Ethics issue and in-depth stories on experimental drugs, AI assisted warfare, microfinance, and more.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 How AI got dragged into the culture wars
Fears about ‘woke’ AI fundamentally misunderstand how it works. Yet they’re gaining traction. (The Guardian) 
+ Why it’s impossible to build an unbiased AI language model. (MIT Technology Review)
 
2 Researchers are racing to understand a new coronavirus variant 
It’s unlikely to be cause for concern, but it shows this virus still has plenty of tricks up its sleeve. (Nature)
+ Covid hasn’t entirely gone away—here’s where we stand. (MIT Technology Review)
+ Why we can’t afford to stop monitoring it. (Ars Technica)
 
3 How Hilary became such a monster storm
Much of it is down to unusually hot sea surface temperatures. (Wired $)
+ The era of simultaneous climate disasters is here to stay. (Axios)
+ People are donning cooling vests so they can work through the heat. (Wired $)
 
4 Brain privacy is set to become important 
Scientists are getting better at decoding our brain data. It’s surely only a matter of time before others want a peek. (The Atlantic $)
+ How your brain data could be used against you. (MIT Technology Review)
 
5 How Nvidia built such a big competitive advantage in AI chips
Today it accounts for 70% of all AI chip sales—and an even greater share for training generative models. (NYT $)
+ The chips it’s selling to China are less effective due to US export controls. (Ars Technica)
+ These simple design rules could turn the chip industry on its head. (MIT Technology Review)
 
6 Inside the complex world of dissociative identity disorder on TikTok 
Reducing stigma is great, but doctors fear people are self-diagnosing or even imitating the disorder. (The Verge)
 
7 What TikTok might have to give up to keep operating in the US
This shows just how hollow the authorities’ purported data-collection concerns really are. (Forbes)
 
8 Soldiers in Ukraine are playing World of Tanks on their phones
It’s eerily similar to the war they are themselves fighting, but they say it helps them to dissociate from the horror. (NYT $)
 
9 Conspiracy theorists are sharing mad ideas on what causes wildfires
But it’s all just a convoluted way to try to avoid having to tackle climate change. (Slate $)
 
10 Christie’s accidentally leaked the location of tons of valuable art 🖼📍
Seemingly thanks to the metadata that often automatically attaches to smartphone photos. (WP $)

Quote of the day

“Is it going to take people dying for something to move forward?”

—An anonymous air traffic controller warns that staffing shortages in their industry, plus other factors, are starting to threaten passenger safety, the New York Times reports.

The big story

Inside effective altruism, where the far future counts a lot more than the present

" "

VICTOR KERLOW

October 2022

Since its birth in the late 2000s, effective altruism has aimed to answer the question “How can those with means have the most impact on the world in a quantifiable way?”—and supplied methods for calculating the answer.

It’s no surprise that effective altruisms’ ideas have long faced criticism for reflecting white Western saviorism, alongside an avoidance of structural problems in favor of abstract math. And as believers pour even greater amounts of money into the movement’s increasingly sci-fi ideals, such charges are only intensifying. Read the full story.

—Rebecca Ackermann

We can still have nice things

A place for comfort, fun and distraction in these weird times. (Got any ideas? Drop me a line or tweet ’em at me.)

+ Watch Andrew Scott’s electrifying reading of the 1965 commencement address ‘Choose One of Five’ by Edith Sampson.
+ Here’s how Metallica makes sure its live performances ROCK. ($)
+ Cannot deal with this utterly ludicrous wooden vehicle. 
+ Learn about a weird and wonderful new instrument called a harpejji.



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Why we should all be rooting for boring AI

Published

1 year ago

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22 August 2023

By

Why we should all be rooting for boring AI


This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

I’m back from a wholesome week off picking blueberries in a forest. So this story we published last week about the messy ethics of AI in warfare is just the antidote, bringing my blood pressure right back up again. 

Arthur Holland Michel does a great job looking at the complicated and nuanced ethical questions around warfare and the military’s increasing use of artificial-intelligence tools. There are myriad ways AI could fail catastrophically or be abused in conflict situations, and there don’t seem to be any real rules constraining it yet. Holland Michel’s story illustrates how little there is to hold people accountable when things go wrong.  

Last year I wrote about how the war in Ukraine kick-started a new boom in business for defense AI startups. The latest hype cycle has only added to that, as companies—and now the military too—race to embed generative AI in products and services. 

Earlier this month, the US Department of Defense announced it is setting up a Generative AI Task Force, aimed at “analyzing and integrating” AI tools such as large language models across the department. 

The department sees tons of potential to “improve intelligence, operational planning, and administrative and business processes.” 

But Holland Michel’s story highlights why the first two use cases might be a bad idea. Generative AI tools, such as language models, are glitchy and unpredictable, and they make things up. They also have massive security vulnerabilities, privacy problems, and deeply ingrained biases.  

Applying these technologies in high-stakes settings could lead to deadly accidents where it’s unclear who or what should be held responsible, or even why the problem occurred. Everyone agrees that humans should make the final call, but that is made harder by technology that acts unpredictably, especially in fast-moving conflict situations. 

Some worry that the people lowest on the hierarchy will pay the highest price when things go wrong: “In the event of an accident—regardless of whether the human was wrong, the computer was wrong, or they were wrong together—the person who made the ‘decision’ will absorb the blame and protect everyone else along the chain of command from the full impact of accountability,” Holland Michel writes. 

The only ones who seem likely to face no consequences when AI fails in war are the companies supplying the technology.

It helps companies when the rules the US has set to govern AI in warfare are mere recommendations, not laws. That makes it really hard to hold anyone accountable. Even the AI Act, the EU’s sweeping upcoming regulation for high-risk AI systems, exempts military uses, which arguably are the highest-risk applications of them all. 

While everyone is looking for exciting new uses for generative AI, I personally can’t wait for it to become boring. 

Amid early signs that people are starting to lose interest in the technology, companies might find that these sorts of tools are better suited for mundane, low-risk applications than solving humanity’s biggest problems.

Applying AI in, for example, productivity software such as Excel, email, or word processing might not be the sexiest idea, but compared to warfare it’s a relatively low-stakes application, and simple enough to have the potential to actually work as advertised. It could help us do the tedious bits of our jobs faster and better.

Boring AI is unlikely to break as easily and, most important, won’t kill anyone. Hopefully, soon we’ll forget we’re interacting with AI at all. (It wasn’t that long ago when machine translation was an exciting new thing in AI. Now most people don’t even think about its role in powering Google Translate.) 

That’s why I’m more confident that organizations like the DoD will find success applying generative AI in administrative and business processes. 

Boring AI is not morally complex. It’s not magic. But it works. 

Deeper Learning

AI isn’t great at decoding human emotions. So why are regulators targeting the tech?

Amid all the chatter about ChatGPT, artificial general intelligence, and the prospect of robots taking people’s jobs, regulators in the EU and the US have been ramping up warnings against AI and emotion recognition. Emotion recognition is the attempt to identify a person’s feelings or state of mind using AI analysis of video, facial images, or audio recordings. 

But why is this a top concern? Western regulators are particularly concerned about China’s use of the technology, and its potential to enable social control. And there’s also evidence that it simply does not work properly. Tate Ryan-Mosley dissected the thorny questions around the technology in last week’s edition of The Technocrat, our weekly newsletter on tech policy.

Bits and Bytes

Meta is preparing to launch free code-generating software
A version of its new LLaMA 2 language model that is able to generate programming code will pose a stiff challenge to similar proprietary code-generating programs from rivals such as OpenAI, Microsoft, and Google. The open-source program is called Code Llama, and its launch is imminent, according to The Information. (The Information) 

OpenAI is testing GPT-4 for content moderation
Using the language model to moderate online content could really help alleviate the mental toll content moderation takes on humans. OpenAI says it’s seen some promising first results, although the tech does not outperform highly trained humans. A lot of big, open questions remain, such as whether the tool can be attuned to different cultures and pick up context and nuance. (OpenAI)

Google is working on an AI assistant that offers life advice
The generative AI tools could function as a life coach, offering up ideas, planning instructions, and tutoring tips. (The New York Times)

Two tech luminaries have quit their jobs to build AI systems inspired by bees
Sakana, a new AI research lab, draws inspiration from the animal kingdom. Founded by two prominent industry researchers and former Googlers, the company plans to make multiple smaller AI models that work together, the idea being that a “swarm” of programs could be as powerful as a single large AI model. (Bloomberg)

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