People learn in different ways. The way we teach should reflect that

Curated on Posted on by Stefaan Verhulst

Article by Jason Williams-Bellamy and Beth Simone Noveck: “There’s never been more hybrid learning in the public sector than today…

There are pros and cons in online and in-person training. But some governments are combining both in a hybrid (also known as blended) learning program. According to the Online Learning Consortium, hybrid courses can be either:

  • A classroom course in which online activity is mixed with classroom meetings, replacing a significant portion, but not all face-to-face activity
  • An online course that is supplemented by required face-to-face instruction such as lectures, discussions, or labs.

A hybrid course can effectively combine the short-term activity of an in-person workshop with the longevity and scale of an online course.

The Digital Leaders program in Israel is a good example of hybrid training. Digital Leaders is a nine-month program designed to train two cohorts of 40 leaders each in digital innovation by means of a regular series of online courses, shared between Israel and a similar program in the UK, interspersed with live workshops. This style of blended learning makes optimal use of participants’ time while also establishing a digital environment and culture among the cohort not seen in traditional programs.

The State government in New Jersey, where I serve as the Chief Innovation Officer, offers a free and publicly accessible online introduction to innovation skills for public servants called the Innovation Skills Accelerator. Those who complete the course become eligible for face-to-face project coaching and we are launching our first skills “bootcamp,” blending online and the face-to-face in Q1 2020.

Blended classrooms have been linked to greater engagement and increased collaboration among participating students. Blended courses allow learners to customise their learning experience in a way that is uniquely best suited for them. One study even found that blended learning improves student engagement and learning even if they only take advantage of the traditional in-classroom resources. While the added complexity of designing for online and off may be off-putting to some, the benefits are clear.

The best way to teach public servants is to give them multiple ways to learn….(More)”.

Human-centred policy? Blending ‘big data’ and ‘thick data’ in national policy

Curated on Posted on by Stefaan Verhulst

Policy Lab (UK): “….Compared with quantitative data, ethnography creates different forms of data – what anthropologists call ‘thick data’. Complex social problems benefit from insights beyond linear, standardised evidence and this is where thick data shows its worth. In Policy Lab we have generated ethnographic films and analysis to sit alongside quantitative data, helping policy-makers to build a rich picture of current circumstances. 

On the other hand, much has been written about big data – data generated through digital interactions – whether it be traditional ledgers and spreadsheets or emerging use of artificial intelligence and the internet of things.  The ever-growing zettabytes of data can reveal a lot, providing a (sometimes real time) digital trail capturing and aggregating our individual choices, preferences, behaviours and actions.  

Much hyped, this quantitative data has great potential to inform future policy, but must be handled ethically, and also requires careful preparation and analysis to avoid biases and false assumptions creeping in. Three issues we have seen in our projects relate to:

  • partial data, for example not having data on people who are not digitally active, biasing the sample
  • the time-consuming challenge of cleaning up data, in a political context where time is often of the essence
  • the lack of data interoperability, where different localities/organisations capture different metrics

Through a number of Policy Lab projects we have used big data to see the big picture before then using thick data to zoom in to the detail of people’s lived experience.  Whereas big data can give us cumulative evidence at a macro, often systemic level, thick data provides insights at an individual or group level.  We have found the blending of ‘big data’ and ‘thick data’ – to be the sweet spot. 

This is a diagram of Policy Lab's model for combining big data and thick data.
Policy Lab’s model for combining big data and thick data (2020)

Policy Lab’s work develops data and insights into ideas for potential policy intervention which we can start to test as prototypes with real people. These operate at the ‘meso’ level (in the middle of the diagram above), informed by both the thick data from individual experiences and the big data at a population or national level. We have written a lot about prototyping for policy and are continuing to explore how you prototype a policy compared to say a digital service….(More)”.

The Wild Wild West of Data Hoarding in the Federal Government

Curated on Posted on by Stefaan Verhulst

ActiveNavigation: “There is a strong belief, both in the public and private sector, that the worst thing you can do with a piece of data is to delete it. The government stores all sorts of data, from traffic logs to home ownership statistics. Data is obviously incredibly important to the Federal Government – but storing large amounts of it poses significant compliance and security risks – especially with the rise of Nation State hackers. As the risk of being breached continues to rise, why is the government not tackling their data storage problem head on?

The Myth of “Free” Storage

Storage is cheap, especially compared to 10-15 years ago. Cloud storage has made it easier than ever to store swaths of information, creating what some call “digital landfills.” However, the true cost of storage isn’t in the ones and zeros sitting on the server somewhere. It’s the business cost.

As information stores continue to grow, the Federal Government’s ability to execute moving information to the correct place gets harder and harder, not to mention more expensive. The U.S. Government has a duty to provide accurate, up-to-date information to its taxpayers – meaning that sharing “bad data” is not an option.

The Association of Information and Image Management (AIIM) reports that half of an organization’s retained data has no value. So far, in 2019, through our work with Federal Agencies, we have discovered that this number, is in fact, low. Over 66% of data we’ve indexed, by the client’s definition, has fallen into that “junk” category. Eliminating junk data paves the way for greater accessibility, transparency and major financial savings. But what is “junk” data?

Redundant, Obsolete and Trivial (ROT) Data

Data is important – but if you can’t assign a value to it, it can become impossible to manage. Simply put, ROT data is digital information that an organization retains, that has no business or legal value. To be efficient from both a cyber hygiene and business perspective, the government needs to get better at purging their ROT data.

Again, purging data doesn’t just help with the hard cost of storage and backups, etc. For example, think about what needs to be done to answer a Freedom of Information Act (FOIA) request. You have a petabyte of data. You have at least a billion documents you need to funnel through to be able to respond to that FOIA request. By eliminating 50% of your ROT data, you probably have also reduced your FOIA response time by 50%.

Records and information governance, taken at face value, might seem fairly esoteric. It may not be as fun or as sexy as the new Space Force, but the reality is, the only way to know if the government is doing what it says it’s through records and information. You can’t answer an FOIA request if there’s no material. You can’t answer Congress if the material isn’t accurate. Being able to access timely, accurate information is critical. That’s why NARA is advocating a move to electronic records.…(More)”.

The future is intelligent: Harnessing the potential of artificial intelligence in Africa

Curated on Posted on by Stefaan Verhulst

Youssef Travaly and Kevin Muvunyi at Brookings: “…AI in particular presents countless avenues for both the public and private sectors to optimize solutions to the most crucial problems facing the continent today, especially for struggling industries. For example, in health care, AI solutions can help scarce personnel and facilities do more with less by speeding initial processing, triage, diagnosis, and post-care follow up. Furthermore, AI-based pharmacogenomics applications, which focus on the likely response of an individual to therapeutic drugs based on certain genetic markers, can be used to tailor treatments. Considering the genetic diversity found on the African continent, it is highly likely that the application of these technologies in Africa will result in considerable advancement in medical treatment on a global level.

In agriculture, Abdoulaye Baniré Diallo, co-founder and chief scientific officer of the AI startup My Intelligent Machines, is working with advanced algorithms and machine learning methods to leverage genomic precision in livestock production models. With genomic precision, it is possible to build intelligent breeding programs that minimize the ecological footprint, address changing consumer demands, and contribute to the well-being of people and animals alike through the selection of good genetic characteristics at an early stage of the livestock production process. These are just a few examples that illustrate the transformative potential of AI technology in Africa.

However, a number of structural challenges undermine rapid adoption and implementation of AI on the continent. Inadequate basic and digital infrastructure seriously erodes efforts to activate AI-powered solutions as it reduces crucial connectivity. (For more on strategies to improve Africa’s digital infrastructure, see the viewpoint on page 67 of the full report). A lack of flexible and dynamic regulatory systems also frustrates the growth of a digital ecosystem that favors AI technology, especially as tech leaders want to scale across borders. Furthermore, lack of relevant technical skills, particularly for young people, is a growing threat. This skills gap means that those who would have otherwise been at the forefront of building AI are left out, preventing the continent from harnessing the full potential of transformative technologies and industries.

Similarly, the lack of adequate investments in research and development is an important obstacle. Africa must develop innovative financial instruments and public-private partnerships to fund human capital development, including a focus on industrial research and innovation hubs that bridge the gap between higher education institutions and the private sector to ensure the transition of AI products from lab to market….(More)”.

On Digital Disinformation and Democratic Myths

Curated on Posted on by Stefaan Verhulst

 David Karpf at MediaWell: “…How many votes did Cambridge Analytica affect in the 2016 presidential election? How much of a difference did the company actually make?

Cambridge Analytica has become something of a Rorschach test among those who pay attention to digital disinformation and microtargeted propaganda. Some hail the company as a digital Svengali, harnessing the power of big data to reshape the behavior of the American electorate. Others suggest the company was peddling digital snake oil, with outlandish marketing claims that bore little resemblance to their mundane product.

One thing is certain: the company has become a household name, practically synonymous with disinformation and digital propaganda in the aftermath of the 2016 election. It has claimed credit for the surprising success of the Brexit referendum and for the Trump digital strategy. Journalists such as Carole Cadwalladr and Hannes Grasseger and Mikael Krogerus have published longform articles that dive into the “psychographic” breakthroughs that the company claims to have made. Cadwalladr also exposed the links between the company and a network of influential conservative donors and political operatives. Whistleblower Chris Wylie, who worked for a time as the company’s head of research, further detailed how it obtained a massive trove of Facebook data on tens of millions of American citizens, in violation of Facebook’s terms of service. The Cambridge Analytica scandal has been a driving force in the current “techlash,” and has been the topic of congressional hearings, documentaries, mass-market books, and scholarly articles.

The reasons for concern are numerous. The company’s own marketing materials boasted about radical breakthroughs in psychographic targeting—developing psychological profiles of every US voter so that political campaigns could tailor messages to exploit psychological vulnerabilities. Those marketing claims were paired with disturbing revelations about the company violating Facebook’s terms of service to scrape tens of millions of user profiles, which were then compiled into a broader database of US voters. Cambridge Analytica behaved unethically. It either broke a lot of laws or demonstrated that old laws needed updating. When the company shut down, no one seemed to shed a tear.

But what is less clear is just how different Cambridge Analytica’s product actually was from the type of microtargeted digital advertisements that every other US electoral campaign uses. Many of the most prominent researchers warning the public about how Cambridge Analytica uses our digital exhaust to “hack our brains” are marketing professors, more accustomed to studying the impact of advertising in commerce than in elections. The political science research community has been far more skeptical. An investigation from Nature magazine documented that the evidence of Cambridge Analytica’s independent impact on voter behavior is basically nonexistent (Gibney 2018). There is no evidence that psychographic targeting actually works at the scale of the American electorate, and there is also no evidence that Cambridge Analytica in fact deployed psychographic models while working for the Trump campaign. The company clearly broke Facebook’s terms of service in acquiring its massive Facebook dataset. But it is not clear that the massive dataset made much of a difference.

At issue in the Cambridge Analytica case are two baseline assumptions about political persuasion in elections. First, what should be our point of comparison for digital propaganda in elections? Second, how does political persuasion in elections compare to persuasion in commercial arenas and marketing in general?…(More)”.

Three Examples of Data Empowerment

Curated on Posted on by Stefaan Verhulst

Blog by Michael Cañares: “It was a humid December afternoon in Banda Aceh, a bustling city in north Indonesia. Two women members of an education reform advocacy group were busy preparing infographics on how the city government was spending its education budget and its impact on service delivery quality in schools. The room was abuzz with questions and apprehension because the next day, the group would present its analysis on the data that they were able to access for the first time to education department officials. The analyses uncovered inefficiencies, poor school performance, ineffective allocation of resources, among others.

While worried about how the officials would react, almost everyone in the room was cheerful. One advocate told me she found the whole process liberating. She found it exhilarating to use government-published data to ask civil servants why the state of education in some schools was disappointing. “Armed with data, I am no longer afraid to speak my mind,” she said.

This was five years ago, but the memory has stuck with me. It was one of many experiences that inspired me to continue advocating for governments to publish data proactively, and searching for ways to use data to strengthen people’s voice on matters that are important to them.

Globally, there are many examples of how data has enabled people to advocate for their rights, demand better public services or hold governments to account. This blog post shares a few examples, focusing largely on how people are able to access and use data that shape their lives — the first dimension of how we characterize data empowerment….

Poverty Stoplight: People use their own data to improve their lives…

Data Zetu: Giving borrowed data back to citizens…

Check My School: Data-based community action to improve school performance…(More)”.

What is My Data Worth?

Curated on Posted on by Stefaan Verhulst

Ruoxi Jia at Berkeley artificial intelligence research: “People give massive amounts of their personal data to companies every day and these data are used to generate tremendous business values. Some economists and politicians argue that people should be paid for their contributions—but the million-dollar question is: by how much?

This article discusses methods proposed in our recent AISTATS and VLDB papers that attempt to answer this question in the machine learning context. This is joint work with David Dao, Boxin Wang, Frances Ann Hubis, Nezihe Merve Gurel, Nick Hynes, Bo Li, Ce Zhang, Costas J. Spanos, and Dawn Song, as well as a collaborative effort between UC Berkeley, ETH Zurich, and UIUC. More information about the work in our group can be found here.

What are the existing approaches to data valuation?

Various ad-hoc data valuation schemes have been studied in the literature and some of them have been deployed in the existing data marketplaces. From a practitioner’s point of view, they can be grouped into three categories:

  • Query-based pricing attaches values to user-initiated queries. One simple example is to set the price based on the number of queries allowed during a time window. Other more sophisticated examples attempt to adjust the price to some specific criteria, such as arbitrage avoidance.
  • Data attribute-based pricing constructs a price model that takes into account various parameters, such as data age, credibility, potential benefits, etc. The model is trained to match market prices released in public registries.
  • Auction-based pricing designs auctions that dynamically set the price based on bids offered by buyers and sellers.

However, existing data valuation schemes do not take into account the following important desiderata:

  • Task-specificness: The value of data depends on the task it helps to fulfill. For instance, if Alice’s medical record indicates that she has disease A, then her data will be more useful to predict disease A as opposed to other diseases.
  • Fairness: The quality of data from different sources varies dramatically. In the worst-case scenario, adversarial data sources may even degrade model performance via data poisoning attacks. Hence, the data value should reflect the efficacy of data by assigning high values to data which can notably improve the model’s performance.
  • Efficiency: Practical machine learning tasks may involve thousands or billions of data contributors; thus, data valuation techniques should be capable of scaling up.

With the desiderata above, we now discuss a principled notion of data value and computationally efficient algorithms for data valuation….(More)”.

Bridging the Elite-Grassroots Divide Among Anticorruption Activists

Curated on Posted on by Stefaan Verhulst

Abigail Bellows at the Carnegie Endowment for International Peace: “Corruption-fueled political change is occurring at a historic rate—but is not necessarily producing the desired systemic reforms. There are many reasons for this, but one is the dramatic dissipation of public momentum after a transition. In countries like Armenia, the surge in civic participation that generated 2018’s Velvet Revolution largely evaporated after the new government assumed power. That sort of civic demobilization makes it difficult for government reformers, facing stubbornly entrenched interests, to enact a transformative agenda.

The dynamics in Armenia reflect a trend across the anticorruption landscape, which is also echoed in other sectors. As the field has become more professionalized, anticorruption nongovernment organizations (NGOs) have developed the legal and technical expertise to serve as excellent counterparts/watchdogs for government. Yet this strength can also be a hurdle when it comes to building credibility with the everyday people they seek to represent. The result is a disconnect between elite and grassroots actors, which is problematic at multiple levels:

  • Technocratic NGOs lack the “people power” to advance their policy recommendations and are exposed to attack as illegitimate or foreign-sponsored.
  • Grassroots networks struggle to turn protest energy into targeted demands and lasting reform, which can leave citizens frustrated and disillusioned about democracy itself.
  • Government reformers lack the sustained popular mandate to deliver on the ambitious agenda they promised, leaving them politically vulnerable to the next convulsion of public anger at corruption.

Two strategies can help civil society address this challenge. First, organizations can seek to hybridize, with in-house capacities for both policy analysis and mass mobilization. Alternatively, organizations can build formal or informal coalitions between groups operating at the elite and grassroots levels, respectively. Both strategies pose challenges: learning new skills, weaving together distinct organizational cultures and methodologies, and defining demands that are both technically sound and publicly appealing. In many instances, coalition-building will be an easier road given it does not require altering internal organizational and personnel structures. Political windows-of-opportunity on anticorruption may lend urgency to this difficult task and help crystallize what both sides have to gain from increased partnership….(More)“.

The Neuroscience of Trust

Curated on Posted on by Stefaan Verhulst

Paul J. Zak at Harvard Business Review: “…About a decade ago, in an effort to understand how company culture affects performance, I began measuring the brain activity of people while they worked. The neuroscience experiments I have run reveal eight ways that leaders can effectively create and manage a culture of trust. I’ll describe those strategies and explain how some organizations are using them to good effect. But first, let’s look at the science behind the framework.

What’s Happening in the Brain

Back in 2001 I derived a mathematical relationship between trust and economic performance. Though my paper on this research described the social, legal, and economic environments that cause differences in trust, I couldn’t answer the most basic question: Why do two people trust each other in the first place? Experiments around the world have shown that humans are naturally inclined to trust others—but don’t always. I hypothesized that there must be a neurologic signal that indicates when we should trust someone. So I started a long-term research program to see if that was true….

How to Manage for Trust

Through the experiments and the surveys, I identified eight management behaviors that foster trust. These behaviors are measurable and can be managed to improve performance.

Recognize excellence.

The neuroscience shows that recognition has the largest effect on trust when it occurs immediately after a goal has been met, when it comes from peers, and when it’s tangible, unexpected, personal, and public. Public recognition not only uses the power of the crowd to celebrate successes, but also inspires others to aim for excellence. And it gives top performers a forum for sharing best practices, so others can learn from them….(More)”.

Why the Global South should nationalise its data

Curated on Posted on by Stefaan Verhulst

Ulises Ali Mejias at AlJazeera: “The recent coup in Bolivia reminds us that poor countries rich in resources continue to be plagued by the legacy of colonialism. Anything that stands in the way of a foreign corporation’s ability to extract cheap resources must be removed.

Today, apart from minerals and fossil fuels, corporations are after another precious resource: Personal data. As with natural resources, data too has become the target of extractive corporate practices.

As sociologist Nick Couldry and I argue in our book, The Costs of Connection: How Data is Colonizing Human Life and Appropriating It for Capitalism, there is a new form of colonialism emerging in the world: data colonialism. By this, we mean a new resource-grab whereby human life itself has become a direct input into economic production in the form of extracted data.

We acknowledge that this term is controversial, given the extreme physical violence and structures of racism that historical colonialism employed. However, our point is not to say that data colonialism is the same as historical colonialism, but rather to suggest that it shares the same core function: extraction, exploitation, and dispossession.

Like classical colonialism, data colonialism violently reconfigures human relations to economic production. Things like land, water, and other natural resources were valued by native people in the precolonial era, but not in the same way that colonisers (and later, capitalists) came to value them: as private property. Likewise, we are experiencing a situation in which things that were once primarily outside the economic realm – things like our most intimate social interactions with friends and family, or our medical records – have now been commodified and made part of an economic cycle of data extraction that benefits a few corporations.

So what could countries in the Global South do to avoid the dangers of data colonialism?…(More)”.