OECD Report: ““Behavioural insights”, or insights derived from the behavioural and social sciences, including decision making, psychology, cognitive science, neuroscience, organisational and group behaviour, are being applied by governments with the aim of making public policies work better. As their use has become more widespread, however, questions are being raised about their effectiveness as well as their philosophical underpinnings. This report discusses the use and reach of behavioural insights, drawing on a comprehensive collection of over 100 applications across the world and policy sectors, including consumer protection, education, energy, environment, finance, health and safety, labour market policies, public service delivery, taxes and telecommunications. It suggests ways to ensure that this experimental approach can be successfully and sustainably used as a public policy tool…(More)”.
Big Data and the Well-Being of Women and Girls: Applications on the Social Scientific Frontier
Report by Bapu Vaitla et al for Data2X: “Conventional forms of data—household surveys, national economic accounts, institutional records, and so on—struggle to capture detailed information on the lives of women and girls. The many forms of big data, from geospatial information to digital transaction logs to records of internet activity, can help close the global gender data gap. This report profiles several big data projects that quantify the economic, social, and health status of women and girls…
This report illustrates the potential of big data in filling the global gender data gap. The rise of big data, however, does not mean that traditional sources of data will become less important. On the contrary, the successful implementation of big data approaches requires investment in proven methods of social scientific research, especially for validation and bias correction of big datasets. More broadly, the invisibility of women and girls in national and international data systems is a political, not solely a technical, problem. In the best case, the current “data revolution” will be reimagined as a step towards better “data governance”: a process through which novel types of information catalyze the creation of new partnerships to advocate for scientific, policy, and political reforms that include women and girls in all spheres of social and economic life….(More)”.
Towards an experimental culture in government: reflections on and from practice
Jesper Christiansen et al at Nesta: “…we share some initial reflections from this work with the hope of prompting a useful discussion about how to articulate the value of experimentation as well as what to consider when strategically planning and doing experiments in government contexts.
Reflection 1: Experimentation as a way of accelerating learning and exploring “the room of the non-obvious”
Governments need to increase their pace and agility in learning about which ideas have the highest potential value-creation and make people’s lives the rationale of governing.
Experimental approaches accelerate learning by systematically testing assumptions and identifying knowledge gaps. What is there to be known about the problem and the function, fit and probability of a suggested solution? Experimentation helps fill these gaps without allocating too much time or resource, and helps governments accelerate the exploration of new potential solution spaces.
This approach is often a key contribution of government policy labs and public sector innovation teams. Units like Lab para la Ciudad in Mexico City, Alberta Co-Lab in Canada, Behavioural insights and Design Unit in Singapore, MindLab in Denmark and Policy Lab in the UK are specifically set up to promote, develop and/or embed experimental approaches and accelerate user-centred learning in different levels of government.
In addition, creating a culture of experimentation extends the policy options available by creating a political environment to test non-linear approaches to wicked problems. In our training, we often distinguish between “the room of the obvious” and the “room of the non-obvious”. By designing portfolios of experiments that include – by deliberate design – the testing of at least some non-linear, non-obvious solutions, government officials can move beyond the automatic mode of many policy interventions and explore the “room of the non-obvious” in a safe-to-fail context (think barbers to prevent suicides or dental insurance to prevent deforestation).
Reflection 2: Experimentation as a way of turning uncertainty into risk
In everyday language, uncertainty and risk are two notions that are often used interchangeably; yet they are very different concepts. Take, for example, the implementation of a solution. Risk is articulated in terms of the probability that the solution will generate a certain outcome. It is measurable (e.g. based on existing data there is X per cent chance of success, or X per cent chance of failure) and qualitative risk factors can be developed and described.
Uncertainty, on the other hand, is a situation where there is a lack of probabilities. There is no prior data on how the solution might perform; future outcomes are not known, and can therefore not be measured. The chance of success can be 0 per cent, 100 per cent, or anything in between (see table below).
There is often talk of the need for government to become more of a ‘risk taker’, or to become better at ‘managing risk’. But as Marco Steinberg, founder of strategic design practice Snowcone & Haystack, recently reminded us, risk-management – where probabilities are known – is actually something that governments do quite well. Issues arise when governments’ legacies can’t shape current solutions: when governments have to deal with the uncertainty of complex challenges by adapting or creating entirely new service systems to fit the needs of our time.
For example, when transforming a health system to fit the needs of our time, little can be known about the probabilities in terms of what might work when establishing a new practice. Or when transforming a social care system to accommodate the lives of vulnerable families, entirely new concepts for solutions need to be explored. “If you don’t have a map showing the way, you have to write one yourself,” as Sam Rye puts it in his inspirational example on the use of experimental cards at The Labs Wananga….
Reflection 3: Experimentation as a way to reframe failure and KPIs
Reflection 4: Experimentation on a continuum between exploration and validation
Reflection 5: Experimentation as cultural change…(More)”.
Ethics of the Health-Related Internet of Things: Mapping the Debate
Paper by Brent Mittelstadt: “The Internet of Things is increasingly spreading into the domain of medical and social care. Internet-enabled devices for monitoring and managing the health and well-being of users outside of traditional medical institutions have rapidly become common tools to support healthcare. Health-related Internet of Things (H-IoT) technologies increasingly play a key role in health management, for purposes including disease prevention, real-time tele-monitoring of patient’s functions, testing of treatments, fitness and well-being monitoring, medication dispensation, and health research data collection. H-IoT promises many benefits for health and healthcare. However, it also raises a host of ethical problems stemming from the inherent risks of Internet enabled devices, the sensitivity of health-related data, and their impact on the delivery of healthcare. This paper maps the main ethical problems that have been identified by the relevant literature and identifies key themes in the on-going debate on ethical problems concerning H-IoT….(More)”
Civic Tech & GovTech: An Overlooked Lucrative Opportunity for Technology Startups
Elena Mesropyan at LTP: “Civic technology, or Civic Tech, is defined as a technology that enables greater participation in government or otherwise assists government in delivering citizen services and strengthening ties with the public. In other words, Civic Tech is where the public lends its talents, usually voluntarily, to help government do a better job. Moreover, Omidyar Network(which invested over $90 million across 35 civic tech organizations over the past decade) emphasizes that like a movement, civic tech is mission-driven, focused on making a change that benefits the public, and in most cases enables better public input into decision making.
As an emerging sector, Civic Tech is defined as incorporating any technology that is used to empower citizens or help make government more accessible, efficient, and effective. Civic tech isn’t just talk, Omidyar notes, it is a community of people coming together to create tangible projects and take action. The civic tech and open data movements have grown with the ubiquity of personal technology.
Civic tech can be defined as a convergence of various fields. An example of such convergence has been given by Knight Foundation, a national foundation with a goal to foster informed and engaged communities to power a healthy democracy:
Source: The Emergence of Civic Tech: Investments in a Growing Field
In the report called Engines of Change: What Civic Tech Can Learn From Social Movements, Civic Tech is divided into three categories:
- Citizen to Citizen (C2C): Technology that improves citizen mobilization or improves connections between citizens
- Citizen to Government (C2G): Technology that improves the frequency or quality of interaction between citizens and government
- Government Technology (Govtech): Innovative technology solutions that make government more efficient and effective at service delivery
In 2015, Forbes reported that Civic Tech makes up almost a quarter of local and state government spendings on technology….
Civic tech initiatives address a diverse range of industries – from energy and payments to agriculture and telecommunications. Mattermark outlines the following top ten industries associated with government and civic tech:
- Finance (OpenGov, Valor Water Analytics, GovSense, Munetrix, PayIt, Meter Feeder, Municode, NIC, etc.)
- Security
- Enterprise Software, Infrastructure (Aclara Technologies)
- Healthcare (MAXIMUS)
- Mobile (Socrata, appcitylife, Passport, etc.)
- Education, Access to Information (ClearGov)
- Marketing
- Data Storage, Management, Analytics (AmigoCloud, Cityworks, FiscalNote, StreetLight Data, Vizalytics Technology,Appallicious, etc.)
- Information Security (EagleEye Intelligence)
…There are certainly much more examples of GovTech/civic tech companies, and just tech startups offering solutions across the board that can significantly improve the way governments are run, and services are delivered to citizens and businesses. More importantly, GovTech should no longer be considered a charity and solely non-profit type of venture. Recently reviewed global P2G payments flows only, for example, are estimated to be at $7.7 trillion and represent a significant feature of the global payments landscape. For the low- and lower-middle-income countries alone, the number hits $375 billion (~50% of annual government expenditure)….(More)”
Technology Use, Exposure to Natural Hazards, and Being Digitally Invisible: Implications for Policy Analytics
Justin Longo, Evan Kuras, Holly Smith, David M. Hondula, and Erik Johnston in Special Issue of Policy & Internet on Data and Policy: “Policy analytics combines new data sources, such as from mobile smartphones, Internet of Everything devices, and electronic payment cards, with new data analytics techniques for informing and directing public policy. However, those who do not own these devices may be rendered digitally invisible if data from their daily actions are not captured. We explore the digitally invisible through an exploratory study of homeless individuals in Phoenix, Arizona, in the context of extreme heat exposure. Ten homeless research participants carried a temperature-sensing device during an extreme heat week, with their individually experienced temperatures (IETs) compared to outdoor ambient temperatures. A nonhomeless, digitally connected sample of 10 university students was also observed, with their IETs analyzed in the same way. Surveys of participants complement the temperature measures. We found that homeless individuals and university students interact differently with the physical environment, experiencing substantial differences in individual temperatures relative to outdoor conditions, potentially leading to differentiated health risks and outcomes. They also interact differently with technology, with the homeless having fewer opportunities to benefit from digital services and lower likelihood to generate digital data that might influence policy analytics. Failing to account for these differences may result in biased policy analytics and misdirected policy interventions….(More)”
Google DeepMind and healthcare in an age of algorithms
Data-driven tools and techniques, particularly machine learning methods that underpin artificial intelligence, offer promise in improving healthcare systems and services. One of the companies aspiring to pioneer these advances is DeepMind Technologies Limited, a wholly-owned subsidiary of the Google conglomerate, Alphabet Inc. In 2016, DeepMind announced its first major health project: a collaboration with the Royal Free London NHS Foundation Trust, to assist in the management of acute kidney injury. Initially received with great enthusiasm, the collaboration has suffered from a lack of clarity and openness, with issues of privacy and power emerging as potent challenges as the project has unfolded. Taking the DeepMind-Royal Free case study as its pivot, this article draws a number of lessons on the transfer of population-derived datasets to large private prospectors, identifying critical questions for policy-makers, industry and individuals as healthcare moves into an algorithmic age….(More)”
Congress Takes Blockchain 101
Mike Orcutt at MIT Technology Review: “Congressman David Schweikert is determined to enlighten his colleagues in Washington about the blockchain. The opportunities the technology creates for society are vast, he says, and right now education is key to keeping the government from “screwing it up.”
Schweikert, a Republican from Arizona, co-chairs the recently launched Congressional Blockchain Caucus. He and fellow co-chair, Democratic Representative Jared Polis of Colorado, say they created it in response to increasing interest and curiosity on Capitol Hill about blockchain technology. “Members of Congress are starting to get visits from people that are doing things with the blockchain and talking about it,” says Polis. “They are interested in learning more, and we hope to provide the forum to do that.”
Blockchain technology is difficult to explain, and misconceptions among policymakers are almost inevitable. One important concept Schweikert says more people need to understand is that a blockchain is not necessarily Bitcoin, and there are plenty of applications of blockchains beyond transferring digital currency. Digital currencies, and especially Bitcoin, the most popular blockchain by far, make some policymakers and government officials wary. But focusing on currency keeps people from seeing the potential the blockchain has to reinvent how we control and manage valuable information, Schweikert argues.
A blockchain is a decentralized, online record-keeping system, or ledger, maintained by a network of computers that verify and record transactions using established cryptographic techniques. Bitcoin’s system, which is open-source, depends on people all around the world called miners. They use specialized computers to verify and record transactions, and receive Bitcoin currency in reward. Several other digital currencies work in a similar fashion.
Digital currency is not the main reason so many institutions have begun experimenting with blockchains in recent years, though. Blockchains can also be used to securely and permanently store other information besides currency transaction records. For instance, banks and other financial companies see this as a way to manage information vital to the transfer of ownership of financial assets more efficiently than they do now. Some experiments have involved the Bitcoin blockchain, some use the newer blockchain software platform called Ethereum, and others have used private or semi-private blockchains.
The government should adopt blockchain technology too, say the Congressmen. A decentralized ledger is better than a conventional database “whenever we need better consumer control of information and security” like in health records, tax returns, voting records, and identity management, says Polis. Several federal agencies and state governments are already experimenting with blockchain applications. The Department of Homeland Security, for example, is running a test to track data from its border surveillance devices in a distributed ledger….
Services for transferring money fall under the jurisdiction of several federal regulators, and face a patchwork of state licensing laws. New blockchain-based business models are challenging traditional notions of money transmission, she says, and many companies are unsure where they fit in the complicated legal landscape.
Boring has argued that financial technology companies would benefit from a regulatory safe zone, or “sandbox”—like those that are already in place in the U.K. and Singapore—where they could test products without the risk of “inadvertent regulatory violations.” We don’t need any new legislation from Congress yet, though—that could stifle innovation even more, she says. “What Congress should be doing is educating themselves on the issues.”…(More)”
Did artificial intelligence deny you credit?
People who apply for a loan from a bank or credit card company, and are turned down, are owed an explanation of why that happened. It’s a good idea – because it can help teach people how to repair their damaged credit – and it’s a federal law, the Equal Credit Opportunity Act. Getting an answer wasn’t much of a problem in years past, when humans made those decisions. But today, as artificial intelligence systems increasingly assist or replace people making credit decisions, getting those explanations has become much more difficult.
Traditionally, a loan officer who rejected an application could tell a would-be borrower there was a problem with their income level, or employment history, or whatever the issue was. But computerized systems that use complex machine learning models are difficult to explain, even for experts.
Consumer credit decisions are just one way this problem arises. Similar concerns exist in health care, online marketing and even criminal justice. My own interest in this area began when a research group I was part of discovered gender bias in how online ads were targeted, but could not explain why it happened.
All those industries, and many others, who use machine learning to analyze processes and make decisions have a little over a year to get a lot better at explaining how their systems work. In May 2018, the new European Union General Data Protection Regulation takes effect, including a section giving people a right to get an explanation for automated decisions that affect their lives. What shape should these explanations take, and can we actually provide them?
Identifying key reasons
One way to describe why an automated decision came out the way it did is to identify the factors that were most influential in the decision. How much of a credit denial decision was because the applicant didn’t make enough money, or because he had failed to repay loans in the past?
My research group at Carnegie Mellon University, including PhD student Shayak Sen and then-postdoc Yair Zick created a way to measure the relative influence of each factor. We call it the Quantitative Input Influence.
In addition to giving better understanding of an individual decision, the measurement can also shed light on a group of decisions: Did an algorithm deny credit primarily because of financial concerns, such as how much an applicant already owes on other debts? Or was the applicant’s ZIP code more important – suggesting more basic demographics such as race might have come into play?…(More)”
Fighting Corruption in Health Care? There’s an App for That
Akjibek Beishebaeva at Voices (OSF): “As an industry that relies heavily on approvals from government officials, the pharmaceutical field in places like Ukraine and Kyrgyzstan—which lack strong mechanisms for public oversight—is particularly susceptible to corruption.
The problem in those countries is exacerbated by the absence of any reliable system to monitor market prices for drugs. For example, a hospital manager bribed by a pharmaceutical representative could agree to procure a drug at a price 10 times higher than at a neighboring hospital. In addition, those medicines procured by the state and meant to be dispensed freely to patients often appear for sale at hospital-based pharmacies instead.
These aren’t victimless crimes. The most needy patients are often the first to suffer when funds are diverted away from lifesaving treatments and medicines.
To tackle this issue, last year the Soros Foundation–Kyrgyzstan and the International Renaissance Foundation jointly conducted the Health Data Hackathon in the Yssyk-Kul region of Kyrgyzstan. Two teams from Ukraine and three teams from Kyrgyzstan—consisting of coders, journalists, and activists—took part. Their goal was to find innovative solutions to address corruption in public procurements and access to health services for vulnerable populations.
Over the two-and-a-half-day effort, one of the Ukrainian teams developed a prototype for a software application to improve the e-tendering platform for all public procurement in Ukraine—ProZorro.
ProZorro itself revolutionized the tender process when it first launched in 2015. It combined a centralized database of online markets and was made accessible to the public. Journalists, activists, and patients today can log in to the system and scrutinize tenders approved by the government. The transparency provided by the system has already shown savings of more than a billion UAH (US$37 million). However, the database is huge and can be tricky to navigate without training.
The application developed at the hackathon makes it even easier to monitor the purchase prices of medicines in Ukraine. Specfically, it will allow users to automatically and instantly compare prices for the same products—a process which previously took many days of manual effort.
The application also offers a more intuitive interface and improved search functionality that will help further reduce corruption and save money—savings that can be redirected towards treatments for people living with HIV, cancer, and hepatitis C. The team is now testing the software and working with the government to introduce it early this year.
Another team came up with the idea to let patients monitor supplies of medicine at facilities in real time. If a hospital representative says that a patient needs to buy drugs that should be readily available, for example, the patient can check online and hold the hospital accountable if the medicines are meant to be provided for free. The tool, called WikiLiky, has already been implemented in the Sumy region of Ukraine.
Likewise, one of the Kyrgyz teams looked at price monitoring in their own country, focusing on the inefficient and mistake-prone acquisition process. For instance, the name of one drug might be misspelled in several different ways, making it difficult to track prices accurately. The team redesigned the functionality of the government e-procurement portal called Codifier, creating uniformity across the system of names, dosages, and other medical specifications….(More)”