How Can We Use Administrative Data to Prevent Homelessness among Youth Leaving Care?

Curated on Posted on by Stefaan Verhulst

Article by Naomi Nichols: “In 2017, I was part of a team of people at the Canadian Observatory on Homelessness and A Way Home Canada who wrote a policy brief titled, Child Welfare and Youth Homelessness in Canada: A proposal for action. Drawing on the results of the first pan-Canadian survey on youth homelessness, Without a Home: The National Youth Homelessness Surveythe brief focused on the disproportionate number of young people who had been involved with child protection services and then later became homeless. Indeed, 57.8% of homeless youth surveyed reported some type of involvement with child protection services over their lifetime. By comparison, in the general population, only 0.3% of young people receive child welfare service. This means, youth experiencing homelessness are far more likely to report interactions with the child welfare system than young people in the general population. 

Where research reveals systematic patterns of exclusion and neglect – that is, where findings reveal that one group is experiencing disproportionately negative outcomes (relative to the general population) in a particular public sector context – this suggests the need for changes in public policy, programming and practice. Since producing this brief, I have been working with an incredibly talented and passionate McGill undergraduate student (who also happens to be the Vice President of Youth in Care Canada), Arisha Khan. Together, we have been exploring just uses of data to better serve the interests of those young people who depend on the state for their access to basic services (e.g., housing, healthcare and food) as well as their self-efficacy and status as citizens. 

One component of this work revolved around a grant application that has just been funded by the Social Sciences and Humanities Research Council of Canada (Data Justice: Fostering equitable data-led strategies to prevent, reduce and end youth homelessness). Another aspect of our work revolved around a policy brief, which we co-wrote and published with the Montreal data-for-good organization, Powered by Data. The brief outlines how a rights-based and custodial approach to administrative data could a) effectively support young people in and leaving care to participate more actively in their transition planning and engage in institutional self-advocacy; and b) enable systemic oversight of intervention implementation and outcomes for young people in and leaving the provincial care system. We produced this brief with the hope that it would be useful to government decision-makers, service providers, researchers, and advocates interested in understanding how institutional data could be used to improve outcomes for youth in and leaving care. In particular, we wanted to explore whether a different orientation to data collection and use in child protection systems could prevent young people from graduating from provincial child welfare systems into homelessness. In addition to this practical concern, we also undertook to think through the ethical and human rights implications of more recent moves towards data-driven service delivery in Canada, focusing on how we might make this move with the best interests of young people in mind. 

As data collection, management and use practices have become more popular, research is beginning to illuminate how these new monitoring, evaluative and predictive technologies are changing governance processes within and across the public sector, as well as in civil society. ….(More)”.

Data Is a Development Issue

Curated on Posted on by Stefaan Verhulst

Paper by Susan Ariel Aaronson: “Many wealthy states are transitioning to a new economy built on data. Individuals and firms in these states have expertise in using data to create new goods and services as well as in how to use data to solve complex problems. Other states may be rich in data but do not yet see their citizens’ personal data or their public data as an asset. Most states are learning how to govern and maintain trust in the data-driven economy; however, many developing countries are not well positioned to govern data in a way that encourages development. Meanwhile, some 76 countries are developing rules and exceptions to the rules governing cross-border data flows as part of new negotiations on e-commerce. This paper uses a wide range of metrics to show that most developing and middle-income countries are not ready or able to provide an environment where their citizens’ personal data is protected and where public data is open and readily accessible. Not surprisingly, greater wealth is associated with better scores on all the metrics. Yet, many industrialized countries are also struggling to govern the many different types and uses of data. The paper argues that data governance will be essential to development, and that donor nations have a responsibility to work with developing countries to improve their data governance….(More)”.

The New York Times thinks a blockchain could help stamp out fake news

Curated on Posted on by Stefaan Verhulst

MIT Technology Review: “Blockchain technology is at the core of a new research project the New York Times has launched, aimed at making “the origins of journalistic content clearer to [its] audience.”

The news: The Times has launched what it calls The News Provenance Project, which will experiment with ways to combat misinformation in the news media. The first project will focus on using a blockchain—specifically a platform designed by IBM—to prove that photos are authentic.

Blockchain? Really? Rumors and speculation swirled in March, after CoinDesk reported that the New York Times was looking for someone to help it develop a “blockchain-based proof-of-concept for news publishers.” Though the newspaper removed the job posting after the article came out, apparently it was serious. In a new blog post, project lead Sasha Koren explains that by using a blockchain, “we might in theory provide audiences with a way to determine the source of a photo, or whether it had been edited after it was published.”

Unfulfilled promise: Using a blockchain to prove the authenticity of journalistic content has long been considered a potential application of the technology, but attempts to do it so far haven’t gotten much traction. If the New York Times can develop a compelling application, it has enough influence to change that….(More)”.

The effective and ethical development of Artificial Intelligence: an opportunity to improve our wellbeing

Curated on Posted on by Stefaan Verhulst

Paper by Toby Walsh, Neil Levy, Genevieve Bell, Anthony Elliott, James Maclaurin, Iven Mareels, Fiona Woods: “As Artificial Intelligence (AI) becomes more advanced its applications will become increasingly complex and will find their place in homes, work places and cities.

AI offers broad-reaching opportunities, but uptake also carries serious implications for human capital, social inclusion, privacy and cultural values to name a few. These must be considered to pre-empt responsible deployment.

This project examined the potential that Artificial Intelligence (AI) technologies have in enhancing Australia’s wellbeing, lifting the economy, improving environmental sustainability and creating a more equitable, inclusive and fair society. Placing society at the core of AI development, the report analyses the opportunities, challenges and prospects that AI technologies present, and explores considerations such as workforce, education, human rights and our regulatory environment.

Key findings:

  1. AI offers major opportunities to improve our economic, societal and environmental wellbeing, while also presenting potentially significant global risks, including technological unemployment and the use of lethal autonomous weapons. Further development of AI must be directed to allow well-considered implementation that supports our society in becoming what we would like it to be – one centred on improving prosperity, reducing inequity and achieving continued betterment.
  2. Proactive engagement, consultation and ongoing communication with the public about the changes and effects of AI will be essential for building community awareness. Earning public trust will be critical to enable acceptance and uptake of the technology.
  3. The application of AI is growing rapidly. Ensuring its continued safe and appropriate development will be dependent on strong governance and a responsive regulatory system that encourages innovation. It will also be important to engender public confidence that the goods and services driven by AI are at, or above, benchmark standards and preserve the values that society seeks.
  4. AI is enabled by access to data. To support successful implementation of AI, there is a need for effective digital infrastructure, including data centres and structures for data sharing, that makes AI secure, trusted and accessible, particularly for rural and remote populations. If such essential infrastructure is not carefully and appropriately developed, the advancement of AI and the immense benefits it offers will be diminished.
  5. Successful development and implementation of AI will require a broad range of new skills and enhanced capabilities that span the humanities, arts and social sciences (HASS) and science, technology, engineering and mathematics (STEM) disciplines. Building a talent base and establishing an adaptable and skilled workforce for the future will need education programs that start in early childhood and continue throughout working life and a supportive immigration policy.
  6. An independently led AI body that brings stakeholders together from government, academia and the public and private sectors would provide a critical mass of skills and institutional leadership to develop AI technologies, as well as promote engagement with international initiatives and to develop appropriate ethical frameworks….(More)”.

The personification of big data

Curated on Posted on by Stefaan Verhulst

Paper by Stevenson, Phillip Douglas and Mattson, Christopher Andrew: “Organizations all over the world, both national and international, gather demographic data so that the progress of nations and peoples can be tracked. This data is often made available to the public in the form of aggregated national level data or individual responses (microdata). Product designers likewise conduct surveys to better understand their customer and create personas. Personas are archetypes of the individuals who will use, maintain, sell or otherwise be affected by the products created by designers. Personas help designers better understand the person the product is designed for. Unfortunately, the process of collecting customer information and creating personas is often a slow and expensive process.

In this paper, we introduce a new method of creating personas, leveraging publicly available databanks of both aggregated national level and information on individuals in the population. A computational persona generator is introduced that creates a population of personas that mirrors a real population in terms of size and statistics. Realistic individual personas are filtered from this population for use in product development…(More)”.

Responding to Some Challenges Posed by the Reidentification of Anonymized Personal Data

Curated on Posted on by Stefaan Verhulst

Paper by Herman T. Tavani and Frances S. Grodzinsky: “In this paper, we examine a cluster of ethical controversies generated by the reidentification of anonymized personal data in the context of big data analytics, with particular attention to the implications for personal privacy. Our paper is organized into two main parts. Part One examines some ethical problems involving re-identification of personally identifiable information (PII) in large data sets. Part Two begins with a brief description of Moor and Weckert’s Dynamic Ethics (DE) and Nissenbaum’s Contextual Integrity (CI) Frameworks. We then investigate whether these frameworks, used together, can provide us with a more robust scheme for analyzing privacy concerns that arise in the re-identification process (as well as within the larger context of big data analytics). This paper does not specifically address re-identification-related privacy concerns that arise in the context of the European Union’s General Data Protection Regulation (GDPR). Instead, we examine those issues in a separate work….(More)”.

“Anonymous” Data Won’t Protect Your Identity

Curated on Posted on by Stefaan Verhulst

Sophie Bushwick at Scientific American: “The world produces roughly 2.5 quintillion bytes of digital data per day, adding to a sea of information that includes intimate details about many individuals’ health and habits. To protect privacy, data brokers must anonymize such records before sharing them with researchers and marketers. But a new study finds it is relatively easy to reidentify a person from a supposedly anonymized data set—even when that set is incomplete.

Massive data repositories can reveal trends that teach medical researchers about disease, demonstrate issues such as the effects of income inequality, coach artificial intelligence into humanlike behavior and, of course, aim advertising more efficiently. To shield people who—wittingly or not—contribute personal information to these digital storehouses, most brokers send their data through a process of deidentification. This procedure involves removing obvious markers, including names and social security numbers, and sometimes taking other precautions, such as introducing random “noise” data to the collection or replacing specific details with general ones (for example, swapping a birth date of “March 7, 1990” for “January–April 1990”). The brokers then release or sell a portion of this information.

“Data anonymization is basically how, for the past 25 years, we’ve been using data for statistical purposes and research while preserving people’s privacy,” says Yves-Alexandre de Montjoye, an assistant professor of computational privacy at Imperial College London and co-author of the new study, published this week in Nature Communications.  Many commonly used anonymization techniques, however, originated in the 1990s, before the Internet’s rapid development made it possible to collect such an enormous amount of detail about things such as an individual’s health, finances, and shopping and browsing habits. This discrepancy has made it relatively easy to connect an anonymous line of data to a specific person: if a private detective is searching for someone in New York City and knows the subject is male, is 30 to 35 years old and has diabetes, the sleuth would not be able to deduce the man’s name—but could likely do so quite easily if he or she also knows the target’s birthday, number of children, zip code, employer and car model….(More)”

The value of data in Canada: Experimental estimates

Curated on Posted on by Stefaan Verhulst

Statistics Canada: “As data and information take on a far more prominent role in Canada and, indeed, all over the world, data, databases and data science have become a staple of modern life. When the electricity goes out, Canadians are as much in search of their data feed as they are food and heat. Consumers are using more and more data that is embodied in the products they buy, whether those products are music, reading material, cars and other appliances, or a wide range of other goods and services. Manufacturers, merchants and other businesses depend increasingly on the collection, processing and analysis of data to make their production processes more efficient and to drive their marketing strategies.

The increasing use of and investment in all things data is driving economic growth, changing the employment landscape and reshaping how and from where we buy and sell goods. Yet the rapid rise in the use and importance of data is not well measured in the existing statistical system. Given the ‘lack of data on data’, Statistics Canada has initiated new research to produce a first set of estimates of the value of data, databases and data science. The development of these estimates benefited from collaboration with the Bureau of Economic Analysis in the United States and the Organisation for Economic Co-operation and Development.

In 2018, Canadian investment in data, databases and data science was estimated to be as high as $40 billion. This was greater than the annual investment in industrial machinery, transportation equipment, and research and development and represented approximately 12% of total non-residential investment in 2018….

Statistics Canada recently released a conceptual framework outlining how one might measure the economic value of data, databases and data science. Thanks to this new framework, the growing role of data in Canada can be measured through time. This framework is described in a paper that was released in The Daily on June 24, 2019 entitled “Measuring investments in data, databases and data science: Conceptual framework.” That paper describes the concept of an ‘information chain’ in which data are derived from everyday observations, databases are constructed from data, and data science creates new knowledge by analyzing the contents of databases….(More)”.

How we can place a value on health care data

Curated on Posted on by Stefaan Verhulst

Report by E&Y: “Unlocking the power of health care data to fuel innovation in medical research and improve patient care is at the heart of today’s health care revolution. When curated or consolidated into a single longitudinal dataset, patient-level records will trace a complete story of a patient’s demographics, health, wellness, diagnosis, treatments, medical procedures and outcomes. Health care providers need to recognize patient data for what it is: a valuable intangible asset desired by multiple stakeholders, a treasure trove of information.

Among the universe of providers holding significant data assets, the United Kingdom’s National Health Service (NHS) is the single largest integrated health care provider in the world. Its patient records cover the entire UK population from birth to death.

We estimate that the 55 million patient records held by the NHS today may have an indicative market value of several billion pounds to a commercial organization. We estimate also that the value of the curated NHS dataset could be as much as £5bn per annum and deliver around £4.6bn of benefit to patients per annum, in potential operational savings for the NHS, enhanced patient outcomes and generation of wider economic benefits to the UK….(More)”.

The Hidden Costs of Automated Thinking

Curated on Posted on by Stefaan Verhulst

Jonathan Zittrain in The New Yorker: “Like many medications, the wakefulness drug modafinil, which is marketed under the trade name Provigil, comes with a small, tightly folded paper pamphlet. For the most part, its contents—lists of instructions and precautions, a diagram of the drug’s molecular structure—make for anodyne reading. The subsection called “Mechanism of Action,” however, contains a sentence that might induce sleeplessness by itself: “The mechanism(s) through which modafinil promotes wakefulness is unknown.”

Provigil isn’t uniquely mysterious. Many drugs receive regulatory approval, and are widely prescribed, even though no one knows exactly how they work. This mystery is built into the process of drug discovery, which often proceeds by trial and error. Each year, any number of new substances are tested in cultured cells or animals; the best and safest of those are tried out in people. In some cases, the success of a drug promptly inspires new research that ends up explaining how it works—but not always. Aspirin was discovered in 1897, and yet no one convincingly explained how it worked until 1995. The same phenomenon exists elsewhere in medicine. Deep-brain stimulation involves the implantation of electrodes in the brains of people who suffer from specific movement disorders, such as Parkinson’s disease; it’s been in widespread use for more than twenty years, and some think it should be employed for other purposes, including general cognitive enhancement. No one can say how it works.

This approach to discovery—answers first, explanations later—accrues what I call intellectual debt. It’s possible to discover what works without knowing why it works, and then to put that insight to use immediately, assuming that the underlying mechanism will be figured out later. In some cases, we pay off this intellectual debt quickly. But, in others, we let it compound, relying, for decades, on knowledge that’s not fully known.

In the past, intellectual debt has been confined to a few areas amenable to trial-and-error discovery, such as medicine. But that may be changing, as new techniques in artificial intelligence—specifically, machine learning—increase our collective intellectual credit line. Machine-learning systems work by identifying patterns in oceans of data. Using those patterns, they hazard answers to fuzzy, open-ended questions. Provide a neural network with labelled pictures of cats and other, non-feline objects, and it will learn to distinguish cats from everything else; give it access to medical records, and it can attempt to predict a new hospital patient’s likelihood of dying. And yet, most machine-learning systems don’t uncover causal mechanisms. They are statistical-correlation engines. They can’t explain why they think some patients are more likely to die, because they don’t “think” in any colloquial sense of the word—they only answer. As we begin to integrate their insights into our lives, we will, collectively, begin to rack up more and more intellectual debt….(More)”.