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)”.

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)”.

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)”.

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)”.

New Data Tools Connect American Workers to Education and Job Opportunities

Curated on Posted on by Stefaan Verhulst

Department of Commerce: “These are the real stories of the people that recently participated in the Census Bureau initiative called The Opportunity Project—a novel, collaborative effort between government agencies, technology companies, and nongovernment organizations to translate government open data into user-friendly tools that solve real world problems for families, communities, and businesses nationwide.  On March 1, they came together to share their projects at The Opportunity Project’s Demo Day. Projects like theirs help veterans, aspiring technologists, and all Americans connect with the career and educational opportunities, like Bryan and Olivia did.

One barrier for many American students and workers is the lack of clear data to help match them with educational opportunities and jobs.  Students want information on the best courses that lead to high paying and high demand jobs. Job seekers want to find the jobs that best match their skills, or where to find new skills that open up career development opportunities.  Despite the increasing availability of big data and the long-standing, highly regarded federal statistical system, there remain significant data gaps about basic labor market questions.

  • What is the payoff of a bachelor’s degree versus an apprenticeship, 2-year degree, industry certification, or other credential?
  • What are the jobs of the future?  Which jobs of today also will be the jobs of the future? What skills and experience do companies value most?

The Opportunity Project brings government, communities, and companies like IBM, the veteran-led Shift.org, and Nepris together to create tools to answer simple questions related to education, employment, health, transportation, housing, and many other matters that are critical to helping Americans advance in their lives and careers….(More)”.

Nearly Half of Canadian Consumers Willing to Share Significant Personal Data with Banks and Insurers in Exchange for Lower Pricing, Accenture Study Finds

Curated on Posted on by Stefaan Verhulst

Press Release: “Nearly half of Canadian consumers would be willing to share significant personal information, such as location data and lifestyle information, with their bank and insurer in exchange for lower pricing on products and services, according to a new report from Accenture (NYSE: ACN).

Consumers willing to share personal data in select scenarios. (CNW Group/Accenture)
Consumers willing to share personal data in select scenarios. (CNW Group/Accenture)

Accenture’s global Financial Services Consumer Study, based on a survey of 47,000 consumers in 28 countries which included 2,000 Canadians, found that more than half of consumers would share that data for benefits including more-rapid loan approvals, discounts on gym memberships and personalized offers based on current location.

At the same time, however, Canadian consumers believe that privacy is paramount, with nearly three quarters (72 per cent) saying they are very cautious about the privacy of their personal data. In fact, data security breaches were the second-biggest concern for consumers, behind only increasing costs, when asked what would make them leave their bank or insurer.

“Canadian consumers are willing to sharing their personal data in instances where it makes their lives easier but remain cautious of exactly how their information is being used,” said Robert Vokes, managing director of financial services at Accenture in Canada. “With this in mind, banks and insurers need to deliver hyper-relevant and highly convenient experience in order to remain relevant, retain trust and win customer loyalty in a digital economy.”

Consumers globally showed strong support for personalized insurance premiums, with 64 per cent interested in receiving adjusted car insurance premiums based on safe driving and 52 per cent in exchange for life insurance premiums tied to a healthy lifestyle. Four in five consumers (79 per cent) would provide personal data, including income, location and lifestyle habits, to their insurer if they believe it would help reduce the possibility of injury or loss.

In banking, 81 per cent of consumers would be willing to share income, location and lifestyle habit data for rapid loan approval, and 76 per cent would do so to receive personalized offers based on their location, such as discounts from a retailer. Approximately two-fifths (42 per cent) of Canadian consumers specifically, want their bank to provide updates on how much money they have based on spending that month and 46 per cent want savings tips based on their spending habits.  

Appetite for data sharing differs around the world

Appetite for sharing significant personal data with financial firms was highest in China, with 67 per cent of consumers there willing to share more data for personalized services. Half (50 per cent) of consumers in the U.S. said they were willing to share more data for personalized services, and in Europe — where the General Data Protection Regulation took effect in May — consumers were more skeptical. For instance, only 40 per cent of consumers in both the U.K. and Germany said they would be willing to share more data with banks and insurers in return for personalized services…(More)”,

Privacy’s not dead. It’s just not evenly distributed

Curated on Posted on by Stefaan Verhulst

Alex Pasternack in Fast Company: “In the face of all the data abuse, many of us have, quite reasonably, thrown up our hands. But privacy didn’t die. It’s just been beaten up, sold, obscured, diffused unevenly across society. What privacy is and why it matters increasingly depends upon who you are, your age, your income, gender, ethnicity, where you’re from, and where you live. To borrow William Gibson’s famous quote about the future and its unevenness and inequalities, privacy is alive—it’s just not evenly distributed. And while we don’t all care about it the same way—we’re even divided on what exactly privacy is—its harms are still real. Even when our own privacy isn’t violated, privacy violations can still hurt us.

Privacy is personal, from the creepy feeling that our phones are literally listening to the endless parade of data breaches that test our ability to care anymore. It’s the unsettling feeling of giving “consent” without knowing what that means, “agreeing” to contracts we didn’t read with companies we don’t really trust. (Forget about understanding all the details; researchers have shown that most privacy policies surpass the reading level of the average person.)

It’s the data about us that’s harvested, bought, sold, and traded by an obscure army of data brokers without our knowledge, feeding marketers, landlords, employers, immigration officials, insurance companies, debt collectors, as well as stalkers and who knows who else. It’s the body camera or the sports arena or the social network capturing your face for who knows what kind of analysis. Don’t think of personal data as just “data.” As it gets more detailed and more correlated, increasingly, our data is us.

And “privacy” isn’t just privacy. It’s also tied up with security, freedom, social justice, free speech, and free thought. Privacy harms aren’t only personal, but societal. It’s not just the multibillion-dollar industry that aims to nab you and nudge you, but the multibillion-dollar spyware industry that helps governments nab dissidents and send them to prison or worse. It’s the supposedly fair and transparent algorithms that aren’t, turning our personal data into risk scores that can help perpetuate race, class, and gender divides, often without our knowing it.

Privacy is about dark ads bought with dark money and the micro-targeting of voters by overseas propagandists or by political campaigns at home. That kind of influence isn’t just the promise of a shadowy Cambridge Analytica or state-run misinformation campaigns, but also the premise of modern-day digital ad campaigns. (Note that Facebook’s research division later hired one of the researchers behind the Cambridge app.) And as the micro-targeting gets more micro, the tech giants that deal in ads are only getting more macro….(More)”

(This story is part of The Privacy Divide, a series that explores the fault lines and disparities–economic, cultural, philosophical–that have developed around digital privacy and its impact on society.)

How AI Can Cure the Big Idea Famine

Curated on Posted on by Stefaan Verhulst

Saahil Jayraj Dama at JoDS: “Today too many people are still deprived of basic amenities such as medicine, while current patent laws continue to convolute and impede innovation. But if allowed, AI can provide an opportunity to redefine this paradigm and be the catalyst for change—if….

Which brings us to the most befitting answer: No one owns the intellectual property rights to AI-generated creations, and these creations fall into the public domain. This may seem unpalatable at first, especially since intellectual property laws have played such a fundamental role in our society so far. We have been conditioned to a point where it seems almost unimaginable that some creations should directly enter the public domain upon their birth.

But, doctrinally, this is the only proposition that stays consistent to extant intellectual property laws. Works created by AI have no rightful owner because the application of mind to generate the creation, along with the actual generation of the creation, would entirely be done by the AI system. Human involvement is ancillary and is limited to creating an environment within which such a creation can take form.

This can be better understood through a hypothetical example: If an AI system were to invent a groundbreaking pharmaceutical ingredient which completely treats balding, then the system would likely begin by understanding the problem and state of prior art. It would undertake research on causes of balding, existing cures, problems with existing cures, and whether its proposed cure would have any harmful side effects. It would also possibly combine research and knowledge across various domains, which could range from Ayurveda to modern-day biochemistry, before developing its invention.

The developer can lay as much stake to this invention as the team behind AlphaGo for beating Lee Sedol at Go. The user is even further detached from the exercise of ingenuity: She would be the person who first thought, “We should build a Go playing AI system,” and direct the AI system to learn Go by watching certain videos and playing against itself. Despite the intervention of all these entities, the fact remains that the victory only belongs to AlphaGo itself.

Doctrinal issues aside, this solution ties in with what people need from intellectual property laws: more openness and accessibility. The demands for improved access to medicines and knowledge, fights against cultural monopolies, and brazen violations of unjust intellectual property laws are all symptomatic of the growing public discontent against strong intellectual property laws. Through AI, we can design legal systems which address these concerns and reform the heavy handed approach that has been adopted toward intellectual property rights so far.

Tying the Threads Together

For the above to materialize, governments and legislators need to accept that our present intellectual property system is broken and inconsistent with what people want. Too many people are being deprived of basic amenities such as medicines, patent trolls and patent thickets are slowing innovation, educational material is still outside the reach of most people, and culture is not spreading as widely as it should. AI can provide an opportunity for us to redefine this paradigm—it can lead to a society that draws and benefits from an enriched public domain.

However, this approach does come with built-in cynicism because it contemplates an almost complete overhaul of the system. One could argue that if open access for AI-generated creations does become the norm, then innovation and creativity would suffer as people would no longer have the incentive to create. People may even refuse to use their AI systems, and instead stick to producing inventions and creative works by themselves. This would be detrimental to scientific and cultural progress and would also slow adoption of AI systems in society.

Yet, judging by the pace at which these systems have progressed so far and what they can currently do, it is easy to imagine a reality where humans developing inventions and producing creative works almost becomes an afterthought. If a machine can access all the world’s publicly available knowledge and information to develop an invention, or study a user’s likes and dislikes while producing a new musical composition, it is easy to see how humans would, eventually, be pushed out of the loop. AI-generated creations are, thus, inevitable.

The incentive theory will have to be reimagined, too. Constant innovation coupled with market forces will change the system from “incentive-to-create” to “incentive-to-create-well.” While every book, movie, song, and invention is treated at par under the law, only the best inventions and creative works will thrive under the new model. If a particular developer’s AI system can write incredible dialogue for a comedy film or invent the most efficient car engines, the market would want more of these AI systems. Thus incentive will not be eliminated, it will just take a different form.

It is true that writing about such grand schemes is significantly tougher than practically implementing them. But, for any idea to succeed, it must start with a discussion such as this one. Admittedly, we are still a moonshot away from any country granting formal recognition to open access as the basis of its intellectual property laws. And even if a country were to do this, it faces a plethora of hoops to jump through, such as conducting feasibility-testing and dealing with international and internal pressure. Despite these issues, facilitating better access through AI systems remains an objective worth achieving for any society that takes pride in being democratic and equal….(More)”.

What Would More Democratic A.I. Look Like?

Curated on Posted on by Stefaan Verhulst

Blog post by Andrew Burgess: “Something curious is happening in Finland. Though much of the global debate around artificial intelligence (A.I.) has become concerned with unaccountable, proprietary systems that could control our lives, the Finnish government has instead decided to embrace the opportunity by rolling out a nationwide educational campaign.

Conceived in 2017, shortly after Finland’s A.I. strategy was announced, the government wants to rebuild the country’s economy around the high-end opportunities of artificial intelligence, and has launched a national programto train 1 percent of the population — that’s 55,000 people — in the basics of A.I. “We’ll never have so much money that we will be the leader of artificial intelligence,” said economic minister Mika Lintilä at the launch. “But how we use it — that’s something different.”

Artificial intelligence can have many positive applications, from being trained to identify cancerous cells in biopsy screenings, predict weather patterns that can help farmers increase their crop yields, and improve traffic efficiency.

But some believe that A.I. expertise is currently too concentrated in the hands of just a few companies with opaque business models, meaning resources are being diverted away from projects that could be more socially, rather than commercially, beneficial. Finland’s approach of making A.I. accessible and understandable to its citizens is part of a broader movement of people who want to democratize the technology, putting utility and opportunity ahead of profit.

This shift toward “democratic A.I.” has three main principles: that all society will be impacted by A.I. and therefore its creators have a responsibility to build open, fair, and explainable A.I. services; that A.I. should be used for social benefit and not just for private profit; and that because A.I. learns from vast quantities of data, the citizens who create that data — about their shopping habits, health records, or transport needs — have a right to say and understand how it is used.

A growing movement across industry and academia believes that A.I. needs to be treated like any other “public awareness” program — just like the scheme rolled out in Finland….(More)”.

Our data, our society, our health: a vision for inclusive and transparent health data science in the UK and Beyond

Curated on Posted on by Stefaan Verhulst

Paper by Elizabeth Ford et al in Learning Health Systems: “The last six years have seen sustained investment in health data science in the UK and beyond, which should result in a data science community that is inclusive of all stakeholders, working together to use data to benefit society through the improvement of public health and wellbeing.

However, opportunities made possible through the innovative use of data are still not being fully realised, resulting in research inefficiencies and avoidable health harms. In this paper we identify the most important barriers to achieving higher productivity in health data science. We then draw on previous research, domain expertise, and theory, to outline how to go about overcoming these barriers, applying our core values of inclusivity and transparency.

We believe a step-change can be achieved through meaningful stakeholder involvement at every stage of research planning, design and execution; team-based data science; as well as harnessing novel and secure data technologies. Applying these values to health data science will safeguard a social license for health data research, and ensure transparent and secure data usage for public benefit….(More)”.