What you don’t know about your health data will make you sick

Curated on Posted on by Stefaan Verhulst

Jeanette Beebe at Fast Company: “Every time you shuffle through a line at the pharmacy, every time you try to get comfortable in those awkward doctor’s office chairs, every time you scroll through the web while you’re put on hold with a question about your medical bill, take a second to think about the person ahead of you and behind you.

Chances are, at least one of you is being monitored by a third party like data analytics giant Optum, which is owned by UnitedHealth Group, Inc. Since 1993, it’s captured medical data—lab results, diagnoses, prescriptions, and more—from 150 million Americans. That’s almost half of the U.S. population.

“They’re the ones that are tapping the data. They’re in there. I can’t remove them from my own health insurance contracts. So I’m stuck. It’s just part of the system,” says Joel Winston, an attorney who specializes in privacy and data protection law.

Healthcare providers can legally sell their data to a now-dizzyingly vast spread of companies, who can use it to make decisions, from designing new drugs to pricing your insurance rates to developing highly targeted advertising.

It’s written in the fine print: You don’t own your medical records. Well, except if you live in New Hampshire. It’s the only state that mandates its residents own their medical data. In 21 states, the law explicitly says that healthcare providers own these records, not patients. In the rest of the country, it’s up in the air.

Every time you visit a doctor or a pharmacy, your record grows. The details can be colorful: Using sources like Milliman’s IntelliScript and ExamOne’s ScriptCheck, a fuller picture of you emerges. Your interactions with the health are system, your medical payments, your prescription drug purchase history. And the market for the data is surging.

Its buyers and sharers—pharma giants, insurers, credit reporting agencies, and other data-hungry companies or “fourth parties” (like Facebook)—say that these massive health data sets can improve healthcare delivery and fuel advances in so-called “precision medicine.”

Still, this glut of health data has raised alarms among privacy advocates, who say many consumers are in the dark about how much of their health-related info is being gathered and mined….

Gardner predicted that traditional health data systems—electronic health records and electronic medical records—are less than ideal, given the “rigidity of the vendors and the products” and the way our data is owned and secured. Don’t count on them being around much longer, she said, “beyond the next few years.”

The future, Gardner suggested, is a system that runs on blockchain, which she defined for the committee as “basically a secure, visible, irrefutable ledger of transactions and ownership.” Still, a recent analysis of over 150 white papers revealed most healthcare blockchain projects “fall somewhere between half-baked and overly optimistic.”

As larger companies like IBM sign on, the technology may be edging closer to reality. Last year, Proof Work outlined a HIPAA-compliant system that manages patients’ medical histories over time, from acute care in the hospital to preventative checkups. The goal is to give these records to patients on their phones, and to create a “democratized ecosystem” to solve interoperability between patients, healthcare providers, insurance companies, and researchers. Similar proposals from blockchain-focused startups like Health Bank and Humanity.co would help patients store and share their health information securely—and sell it to researchers, too….(More)”.

Technology and political will can create better governance

Curated on Posted on by Stefaan Verhulst

Darshana Narayanan at The Economist: “Current forms of democracy exclude most people from political decision-making. We elect representatives and participate in the occasional referendums, but we mainly remain on the outside. The result is that a handful of people in power dictate what ought to be collective decisions. What we have now is hardly a democracy, or at least, not a democracy that we should settle for.

To design a truer form of democracy—that is, fair representation and an outcome determined by a plurality—we might draw some lessons from the collective behaviour of other social animals: schools of fish, for example. Schooling fish self-organise for the benefit of the group and are rarely in a fracas. Individuals in the group may not be associated and yet they reach consensus. A study in 2011 led by Iain Couzin found that “uninformed” fish—in that case, ones that had not been trained to have a preference to move towards a particular target—can dilute the influence of a powerful minority group which did have such preferences. 

Of course fish are not the same as humans. But that study does suggest a way of thinking about decision-making. Instead of limiting influence to experts and strongly motivated interest groups, we should actively work to broaden participation to ensure that we include people lacking strong preferences or prior knowledge of an issue. In other words, we need to go against the ingrained thinking that non-experts should be excluded from decision-making. Inclusivity might just improve our chances of reaching a real, democratic consensus.

How can our political institutions facilitate this? In my work over the past several years I have tried to apply findings from behavioural science into institutions and into code to create better systems of governance. In the course of my work, I have found some promising experiments taking place around the world that harness new digital tools. They point the way to how democracy can be practiced in the 21st century….(More)”.

Catch Me Once, Catch Me 218 Times

Curated on Posted on by Stefaan Verhulst

Josh Kaplan at Slate: “…It was 2010, and the San Diego County Sheriff’s Department had recently rolled out a database called GraffitiTracker—software also used by police departments in Denver and Los Angeles County—and over the previous year, they had accumulated a massive set of images that included a couple hundred photos with his moniker. Painting over all Kyle’s handiwork, prosecutors claimed, had cost the county almost $100,000, and that sort of damage came with life-changing consequences. Ultimately, he made a plea deal: one year of incarceration, five years of probation, and more than $87,000 in restitution.

Criticism of police technology often gets mired in the complexities of the algorithms involved—the obscurity of machine learning, the feedback loops, the potentials for racial bias and error. But GraffitiTracker can tell us a lot about data-driven policing in part because the concept is so simple. Whenever a public works crew goes to clean up graffiti, before they paint over it, they take a photo and put it in the county database. Since taggers tend to paint the same moniker over and over, now whenever someone is caught for vandalism, police can search the database for their pseudonym and get evidence of all the graffiti they’ve ever done.

In San Diego County, this has radically changed the way that graffiti is prosecuted and has pumped up the punishment for taggers—many of whom are minors—to levels otherwise unthinkable. The results have been lucrative. In 2011, the first year San Diego started using GraffitiTracker countywide (a few San Diego jurisdictions already had it in place), the amount of restitution received for graffiti jumped from about $170,000 to almost $800,000. Roughly $300,000 of that came from juvenile cases. For the jurisdictions that weren’t already using GraffitiTracker, the jump was even more stark: The annual total went from $45,000 to nearly $400,000. In these cities, the average restitution ordered in adult cases went from $1,281 to $5,620, and at the same time, the number of cases resulting in restitution tripled. (San Diego has said it makes prosecuting vandalism easier.)

Almost a decade later, San Diego County and other jurisdictions are still using GraffitiTracker, yet it’s received very little media attention, despite the startling consequences for vandalism prosecution. But its implications extend far beyond tagging. GraffitiTracker presaged a deeper problem with law enforcement’s ability to use technology to connect people to crimes that, as Deputy District Attorney Melissa Ocampo put it to me, “they thought they got away with.”…(More)”.

The Referendum and Other Essays on Constitutional Politics

Curated on Posted on by Stefaan Verhulst

Book by Matt Qvortrup: “Until recently, referendums were little used. After the Scottish independence and Brexit referendums, they have come to the fore as a mechanism with the potential to disrupt the status quo and radically change political direction. This book looks at the historical development of the referendum, its use in different jurisdictions, and the types of constitutional questions it seeks to address. Written in an engaging style, the book offers a clear, objective overview of this important political and constitutional tool….(More)”.

Seeing, Naming, Knowing

Curated on Posted on by Stefaan Verhulst

Essay by Nora N. Khan for Brooklyn Rail: “…. Throughout this essay, I use “machine eye” as a metaphor for the unmoored orb, a kind of truly omnidirectional camera (meaning, a camera that can look in every direction and vector that defines the dimensions of a sphere), and as a symbolic shorthand for the sum of four distinct realms in which automated vision is deployed as a service. (Vision as a Service, reads the selling tag for a new AI surveillance camera company).10 Those four general realms are: 

1. Massive AI systems fueled by the public’s flexible datasets of their personal images, creating a visual culture entirely out of digitized images. 

2. Facial recognition technologies and neural networks improving atop their databases. 

3. The advancement of predictive policing to sort people by types. 

4. The combination of location-based tracking, license plate-reading, and heat sensors to render skein-like, live, evolving maps of people moving, marked as likely to do X.

Though we live the results of its seeing, and its interpretation of its seeing, for now I would hold on blaming ourselves for this situation. We are, after all, the living instantiations of a few thousand years of such violent seeing globally, enacted through imperialism, colonialism, caste stratification, nationalist purges, internal class struggle, and all the evolving theory to support and galvanize the above. Technology simply recasts, concentrates, and amplifies these “tendencies.” They can be hard to see at first because the eye’s seeing seems innocuous, and is designed to seem so. It is a direct expression of the ideology of software, which reflects its makers’ desires. These makers are lauded as American pioneers, innovators, genius-heroes living in the Bay Area in the late 1970s, vibrating at a highly specific frequency, the generative nexus of failed communalism and an emerging Californian Ideology. That seductive ideology has been exported all over the world, and we are only now contending with its impact.

Because the workings of machine visual culture are so remote from our sense perception, and because it so acutely determines our material (economic, social), and affective futures, I invite you to see underneath the eye’s outer glass shell, its holder, beyond it, to the grid that organizes its “mind.” That mind simulates a strain of ideology about who exactly gets to gather data about those on that grid below, and how that data should be mobilized to predict the movements and desires of the grid dwellers. This mind, a vast computational regime we are embedded in, drives the machine eye. And this computational regime has specific values that determine what is seen, how it is seen, and what that seeing means….(More)”.

OECD survey reveals many people unhappy with public services and benefits

Curated on Posted on by Stefaan Verhulst

Report by OECD: “Many people in OECD countries believe public services and social benefits are inadequate and hard to reach. More than half say they do not receive their fair share of benefits given the taxes they pay, and two-thirds believe others get more than they deserve. Nearly three out of four people say they want their government to do more to protect their social and economic security.  

These are among the findings of a new OECD survey, “Risks that Matter”, which asked over 22,000 people aged 18 to 70 years old in 21 countries about their worries and concerns and how well they think their government helps them tackle social and economic risks.

This nationally representative survey finds that falling ill and not being able to make ends meet are often at the top of people’s lists of immediate concerns. Making ends meet is a particularly common worry for those on low incomes and in countries that were hit hard by the financial crisis. Older people are most often worried about their health, while younger people are frequently concerned with securing adequate housing. When asked about the longer-term, across all countries, getting by in old age is the most commonly cited worry.

The survey reveals a dissatisfaction with current social policy. Only a minority are satisfied with access to services like health care, housing, and long-term care. Many believe the government would not be able to provide a proper safety net if they lost their income due to job loss, illness or old age. More than half think they would not be able to easily access public benefits if they needed them.

“This is a wake-up call for policy makers,” said OECD Secretary-General Angel Gurría. “OECD countries have some of the most advanced and generous social protection systems in the world. They spend, on average, more than one-fifth of their GDP on social policies. Yet, too many people feel they cannot count fully on their government when they need help. A better understanding of the factors driving this perception and why people feel they are struggling is essential to making social protection more effective and efficient. We must restore trust and confidence in government, and promote equality of opportunity.”

In every country surveyed except Canada, Denmark, Norway and the Netherlands, most people say that their government does not incorporate the views of people like them when designing social policy. In a number of countries, including Greece, Israel, Lithuania, Portugal and Slovenia, this share rises to more than two-thirds of respondents. This sense of not being part of the policy debate increases at higher levels of education and income, while feelings of injustice are stronger among those from high-income households.

Public perceptions of fairness are worrying. More than half of respondents say they do not receive their fair share of benefits given the taxes they pay, a share that rises to three quarters or more in Chile, Greece, Israel and Mexico. At the same time, people are calling for more help from government. In almost all countries, more than half of respondents say they want the government to do more for their economic and social security. This is especially the case for older respondents and those on low incomes.

Across countries, people are worried about financial security in old age, and most are willing to pay more to support public pension systems… (More)”.

The Future of Government 2030+

Curated on Posted on by Stefaan Verhulst

Report by Lucia Vesnic Alujevic, Eckhard Stoermer, Jennifer-Ellen Rudkin, Fabiana Scapolo and Lucy Kimbell: “The Future of Government 2030+: A Citizen Centric Perspective on New Government Models project brings citizens to the centre of the scene. The objective of this project is to explore the emerging societal challenges, analyse trends in a rapidly changing digital world and launch an EU-wide debate on the possible future government models. To address this, citizen engagement, foresight and design are combined, with recent literature from the field of digital politics and media as a framework. The main research question of the project is: How will citizens, together with other actors, shape governments, policies and democracy in 2030 and beyond? Throughout the highly participatory process, more than 150 citizens, together with CSO, think tank, business and public sector representatives, as well as 100 design students participated in the creation of future scenarios and concepts. Four scenarios have been created using the 20 stories emerged from citizen workshops. They served as an inspiration for design students to develop 40 FuturGov concepts. Through the FuturGov Engagement Game, the project’s ambition is to trigger and launch a debate with citizens, businesses, civil society organizations, policy-makers and civil servants in Europe….(More)”.

The Bad Pupil

Curated on Posted on by Stefaan Verhulst

CCCBLab: “In recent years we have been witnessing a constant trickle of news on artificial intelligence, machine learning and computer vision. We are told that machines learn, see, create… and all this builds up a discourse based on novelty, on a possible future and on a series of worries and hopes. It is difficult, sometimes, to figure out in this landscape which are real developments, and which are fantasies or warnings. And, undoubtedly, this fog that surrounds it forms part of the power that we grant, both in the present and on credit, to these tools, and of the negative and positive concerns that they arouse in us. Many of these discourses may fall into the field of false debates or, at least, of the return of old debates. Thus, in the classical artistic field, associated with the discourse on creation and authorship, there is discussion regarding the entity to be awarded to the images created with these tools. (Yet wasn’t the argument against photography in art that it was an image created automatically and without human participation? And wasn’t that also an argument in favour of taking it and using it to put an end to a certain idea of art?)

Metaphors are essential in the discourse on all digital tools and the power that they have. Are expressions such as “intelligence”, “vision”, “learning”, “neural” and the entire range of similar words the most adequate for defining these types of tools? Probably not, above all if their metaphorical nature is sidestepped. We would not understand them in the same way if we called them tools of probabilistic classification or if instead of saying that an artificial intelligence “has painted” a Rembrandt, we said that it has produced a statistical reproduction of his style (something which is still surprising, and to be celebrated, of course). These names construct an entity for these tools that endows them with a supposed autonomy and independence upon which their future authority is based.

Because that is what it’s about in many discourses: constructing a characterisation that legitimises an objective or non-human capacity in data analysis….

We now find ourselves in what is, probably, the point of the first cultural reception of these tools. Of their development in fields of research and applications that have already been derived, we are moving on to their presence in the public discourse. It is in this situation and context, where we do not fully know the breadth and characteristics of these technologies (meaning fears are more abstract and diffuse and, thus, more present and powerful), when it is especially important to understand what we are talking about, to appropriate the tools and to intervene in the discourses. Before their possibilities are restricted and solidified until they seem indisputable, it is necessary to experiment with them and reflect on them; taking advantage of the fact that we can still easily perceive them as in creation, malleable and open.

In our projects The Bad Pupil. Critical pedagogy for artificial intelligences and Latent Spaces. Machinic Imaginations we have tried to approach to these tools and their imaginary. In the statement of intentions of the former, we expressed our desire, in the face of the regulatory context and the metaphor of machine learning, to defend the bad pupil as one who escapes the norm. And also how, faced with an artificial intelligence that seeks to replicate the human on inhuman scales, it is necessary to defend and construct a non-mimetic one that produces unexpected relations and images.

Fragment of De zeven werken van barmhartigheid, Meester van Alkmaar, 1504 (Rijksmuseum, Amsterdam) analysed with YOLO9000 | The Bad Pupil - Estampa

Fragment of De zeven werken van barmhartigheid, Meester van Alkmaar, 1504 (Rijksmuseum, Amsterdam) analysed with YOLO9000 | The Bad Pupil – Estampa

Both projects are also attempts to appropriate these tools, which means, first of all, escaping industrial barriers and their standards. In this field in which mass data are an asset within reach of big companies, employing quantitively poor datasets and non-industrial calculation potentials is not just a need but a demand….(More)”.

Imagination unleashed: Democratising the knowledge economy

Curated on Posted on by Stefaan Verhulst

Report by Roberto Mangabeira Unger, Isaac Stanley, Madeleine Gabriel, and Geoff Mulgan: “If economic eras are defined by their most advanced form of production, then we live in a knowledge economy – one where knowledge plays a decisive role in the organisation of production, distribution and consumption.

The era of Fordist mass production that preceded it transformed almost every part of the economy. But the knowledge economy hasn’t spread in the same way. Only some people and places are reaping the benefits.

This is a big problem: it contributes to inequality, stagnation and political alienation. And traditional policy solutions are not sufficient to tackle it. We can’t expect benefits simply to trickle down to the rest of the population, and redistribution alone will not solve the inequalities we are facing.

What’s the alternative? Nesta has been working with Roberto Mangabeira Unger to convene discussions with politicians, researchers, and activists from member countries of the Organisation for Economic Co-operation and Development, to explore policy options for an inclusive knowledge economy. This report presents the results of that collaboration.

We argue that an inclusive knowledge economy requires action to democratise the economy – widening access to capital and productive opportunity, transforming models of ownership, addressing new concentrations of power, and democratising the direction of innovation.

It demands that we establish a social inheritance by reforming education and social security.

And it requires us to create a high-energy democracy, promoting experimental government, and independent and empowered civil society.

Recommendations

This is a broad ranging agenda. In practice, it focuses on:

  • SMEs and their capacity and skills – greatly accelerating the adoption of new methods and technologies at every level of the economy, including new clean technologies that reduce carbon emissions
  • Transforming industrial policy to cope with the new concentrations of power and to prevent monopoly and predatory behaviours
  • Transforming and disaggregating property rights so that more people can have a stake in productive resources
  • Reforming education to prepare the next generation for the labour market of the future not the past – cultivating the mindsets, skills and cultures relevant to future jobs
  • Reforming social policy to respond to new patterns of work and need – creating more flexible systems that can cope with rapid change in jobs and skills, with a greater emphasis on reskilling
  • Reforming government and democracy to achieve new levels of participation, agility, experimentation and effectiveness…(More)”

Comparative Accuracy of Diagnosis by Collective Intelligence of Multiple Physicians vs Individual Physicians

Curated on Posted on by Stefaan Verhulst

Study by Michael L. Barnett et al in JAMA: “Is a collective intelligence approach of pooling multiple clinician and medical student diagnoses associated with improvement in diagnostic accuracy in online, structured clinical cases?

Findings  This cross-sectional study analyzing data from the Human Diagnosis Project found that, across a broad range of medical cases and common presenting symptoms, independent differential diagnoses of multiple physicians combined into a weighted list significantly outperformed diagnoses of individual physicians with groups as small as 2, and accuracy increased with larger groups up to 9 physicians. Groups of nonspecialists also significantly outperformed individual specialists solving cases matched to the individual specialist’s specialty….

Main Outcomes and Measures  The primary outcome was diagnostic accuracy, assessed as a correct diagnosis in the top 3 ranked diagnoses for an individual; for groups, the top 3 diagnoses were a collective differential generated using a weighted combination of user diagnoses with a variety of approaches. A version of the McNemar test was used to account for clustering across repeated solvers to compare diagnostic accuracy.

Conclusions and Relevance  A collective intelligence approach was associated with higher diagnostic accuracy compared with individuals, including individual specialists whose expertise matched the case diagnosis, across a range of medical cases. Given the few proven strategies to address misdiagnosis, this technique merits further study in clinical settings….(More)”.