Data Africa


Data Africa is an open data platform designed to provide information on key themes for research and development such as: agriculture, climate, poverty and child health across Sub-Saharan Africa at the sub-national level. The main goal of the online tool is to present the themes to a wide, even non-technical audience through easily accessible visual narratives.

In its first stage, the platform is focused on national and sub-national level data for 13 countries:

  • Burkina Faso
  • Ethiopia
  • Ghana
  • Kenya
  • Malawi
  • Mali
  • Mozambique
  • Nigeria
  • Rwanda
  • Senegal
  • Tanzania
  • Uganda
  • Zambia

Over time, we anticipate expanding the coverage of the platform with additional countries and increasing the amount of data available through the platform….

The data contained in the online tool draws from a variety of sources, including:

The Implementation of Open Data in Indonesia


Paper by Dani Gunawan and Amalia Amalia: “Nowadays, public demands easy access to nonconfidential government data, such as public digital information on health, industry, and culture that can be accessed on the Internet. This will lead departments within government to be efficient and more transparent. As the results, rapid development of applications will solve citizens’ problems in many sectors. One Data Initiatives is the prove that the Government of Indonesia supports data transparency. This research investigates the implementation of open data in Indonesia based on Tim BernersLee five-star rating and open stage model by Kalampokis. The result shows that mostly data in Indonesia is freely available in the Internet, but most of them are not machine-readable and do not support non-proprietary format. The drawback of Indonesia’s open data is lack of ability to link the existing data with other data sources. Therefore, Indonesia is still making initial steps with data inventories and beginning to publish key datasets of public interest…(More)”

Rage against the machines: is AI-powered government worth it?


Maëlle Gavet at the WEF: “…the Australian government’s new “data-driven profiling” trial for drug testing welfare recipients, to US law enforcement’s use of facial recognition technology and the deployment of proprietary software in sentencing in many US courts … almost by stealth and with remarkably little outcry, technology is transforming the way we are policed, categorized as citizens and, perhaps one day soon, governed. We are only in the earliest stages of so-called algorithmic regulation — intelligent machines deploying big data, machine learning and artificial intelligence (AI) to regulate human behaviour and enforce laws — but it already has profound implications for the relationship between private citizens and the state….

Some may herald this as democracy rebooted. In my view it represents nothing less than a threat to democracy itself — and deep scepticism should prevail. There are five major problems with bringing algorithms into the policy arena:

  1. Self-reinforcing bias…
  2. Vulnerability to attack…
  3. Who’s calling the shots?…
  4. Are governments up to it?…
  5. Algorithms don’t do nuance….

All the problems notwithstanding, there’s little doubt that AI-powered government of some kind will happen. So, how can we avoid it becoming the stuff of bad science fiction? To begin with, we should leverage AI to explore positive alternatives instead of just applying it to support traditional solutions to society’s perceived problems. Rather than simply finding and sending criminals to jail faster in order to protect the public, how about using AI to figure out the effectiveness of other potential solutions? Offering young adult literacy, numeracy and other skills might well represent a far superior and more cost-effective solution to crime than more aggressive law enforcement. Moreover, AI should always be used at a population level, rather than at the individual level, in order to avoid stigmatizing people on the basis of their history, their genes and where they live. The same goes for the more subtle, yet even more pervasive data-driven targeting by prospective employers, health insurers, credit card companies and mortgage providers. While the commercial imperative for AI-powered categorization is clear, when it targets individuals it amounts to profiling with the inevitable consequence that entire sections of society are locked out of opportunity….(More)”.

Government innovations and the hype cycle


Danny Buerkli at the Centre for Public Impact: “The Gartner hype cycle tracks how technologies develop from initial conception to productive use. There is much excitement around different methodologies and technologies in the “government innovation” space, but which of these is hyped and which of these is truly productive?

Last year we made some educated guesses and placed ten government innovations along the hype cycle. This year, however, we went for something bigger and better. We created an entirely non-scientific poll and asked respondents to tell us where they thought these same ten government innovations sat on the hype cycle.

The innovations we included were artificial intelligence, blockchain, design thinking, policy labs, behavioural insights, open data, e-government, agile, lean and New Public Management.

Here is what we learned.

  1. For the most part, we’re still in the early days

On average, our respondents don’t think that any of the methods have made it into truly productive use. In fact, for seven out of the ten innovations, the majority of respondents believed that these were indeed still in the “technology trigger” phase.

Assuming that these innovations will steadily make their way along the hype cycle, we should expect a lot more hype (as they enter the “peak of inflated expectations”) and a lot more disappointment (as they descend into the “trough of disillusionment)” going forward. Government innovation advocates should take heed.

  1. Policy Labs are believed to be in “peak of inflated expectations”

This innovation attracted the highest level of disagreement from respondents. While almost two out of five people believe that policy labs are in the “technology trigger” phase, one out of five see them as having already reached the “slope of enlightenment”. On average, however, respondents believe policy labs to be in the “peak of inflated expectations”….

  1. Blockchain is seen as the most nascent government innovation

Our survey respondents rather unanimously believe that blockchain is at the very early stage of the “technology trigger” phase. Given that blockchain is often characterized as a solution in search of a problem, this view may not be surprising. The survey results also indicates that blockchain will have a long way to go before it will be used productively in government, but there are several ways this can be done.

  1. Artificial intelligence inspires a lot of confidence (in some)
  1. New Public Management is – still – overhyped?… (More).

‘I’ve Got Nothing to Hide’ and Other Misunderstandings of Privacy


“In this short essay, written for a symposium in the San Diego Law Review, Professor Daniel Solove examines the nothing to hide argument. When asked about government surveillance and data mining, many people respond by declaring: “I’ve got nothing to hide.” According to the nothing to hide argument, there is no threat to privacy unless the government uncovers unlawful activity, in which case a person has no legitimate justification to claim that it remain private. The nothing to hide argument and its variants are quite prevalent, and thus are worth addressing. In this essay, Solove critiques the nothing to hide argument and exposes its faulty underpinnings….(More)”

Global innovations in measurement and evaluation


Report by Andrew WestonAnne KazimirskiAnoushka KenleyRosie McLeodRuth Gripper: “Measurement and evaluation is core to good impact practice. It helps us understand what works, how it works and how we can achieve more. Good measurement and evaluation involves reflective, creative, and proportionate approaches. It makes the most of existing theoretical frameworks as well as new digital solutions, and focuses on learning and improving. We researched the latest changes in theory and practice based on both new and older, renascent ideas. We spoke to leading evaluation experts from around the world, to ask what’s exciting them, what people are talking about and what is most likely to make a long lasting contribution to evaluation. And we found that new thinking, techniques, and technology are influencing and improving practice.

Technology is enabling us to gather different types of data on bigger scales, helping us gain insights or spot patterns we could not see before. Advances in systems to capture, manage and share sensitive data are helping organisations that want to work collaboratively, while moves towards open data are providing better access to data that can be linked together to generate even greater insight. Traditional models of evaluating a project once it has finished are being overtaken by methods that feed more dynamically into service design. We are learning from the private sector, where real-time feedback shapes business decisions on an ongoing basis asking: ‘is it working?’ instead of ‘did it work?’.

And approaches that focus on assessing not just if something works but how and why, for whom, and under what conditions are also generating more insight into the effectiveness of programmes. Technology may be driving many of the innovations we highlight here, but some of the most exciting developments are happening because of changes in the ideologies and cultures that inform our approach to solving big problems. This is resulting in an increased focus on listening to and involving users, and on achieving change at a systemic level—with technology simply facilitating these changes.

Some of the pressures that compel measurement and evaluation activity remain misguided. For example, there can be too big a focus on obtaining a cost-benefit ratio—regardless of the quality of the data it is based on—and not enough encouragement from funders for charities to learn from their evaluation activity. Even the positive developments have their pitfalls: new technologies pose new data protection risks, ethical hazards, and the possibility of exclusion if participation requires high levels of technical ability. It is important that, as the field develops and capabilities increase, we remain focused on achieving best practice.

This report highlights the developments that we think have the greatest potential to improve evaluation and programme design, and the careful collection and use of data. We want to celebrate what is possible, and encourage wider application of these ideas. Choosing the innovations In deciding which trends to include in this report, we considered how different approaches contributed to better evaluation by:

  • overcoming previous barriers to good evaluation practice, eg, through new technologies or skills;
  • providing more meaningful or robust data;
  • using data to support decision-making, learning and improving practice;
  • increasing equality between users, service deliverers and funders; and
  • offering new contexts for collaboration that improve the utility of data.

… Eight key trends emerged from our research that we thought to be most exciting, relevant and likely to have a long-lasting contribution. Some of these are driven by cutting-edge technology; others reflect growing application of ideas that push practice beyond ‘traditional’ models of evaluation. User-centric and shared approaches are leading to better informed measurement and evaluation design. Theory-based evaluation and impact management embolden us to ask better research questions and obtain more useful answers. Data linkage, the availability of big data, and the possibilities arising from remote sensing are increasing the number of questions we can answer. And data visualisation opens up doors to better understanding and communication of this data. Here we present each of these eight innovations and showcase examples of how organisations are using them to better understand and improve their work….(More)”

Digital transformation’s people problem


Jen Kelchner at open source: …Arguably, the greatest chasm we see in our organizational work today is the actual transformation before, during, or after the implementation of a digital technology—because technology invariably crosses through and impacts people, processes, and culture. What are we transforming from? What are we transforming into? These are “people issues” as much as they are “technology issues,” but we too rarely acknowledge this.

Operating our organizations on open principles promises to spark new ways of thinking that can help us address this gap. Over the course of this three-part series, we’ll take a look at how the principle foundations of open play a major role in addressing the “people part” of digital transformation—and closing that gap before and during implementations.

The impact of digital transformation

The meaning of the term “digital transformation” has changed considerably in the last decade. For example, if you look at where organizations were in 2007, you’d watch them grapple with the first iPhone. Focus here was more on search engines, data mining, and methods of virtual collaboration.

A decade later in 2017, however, we’re investing in artificial intelligence, machine learning, and the Internet of Things. Our technologies have matured—but our organizational and cultural structures have not kept pace with them.

Value Co-creation In The Organizations of the Future, a recent research report from Aalto University, states that digital transformation has created opportunities to revolutionize and change existing business models, socioeconomic structures, legal and policy measures, organizational patterns, and cultural barriers. But we can only realize this potential if we address both the technological and the organizational aspects of digital transformation.

Four critical areas of digital transformation

Let’s examine four crucial elements involved in any digital transformation effort:

  • change management
  • the needs of the ecosystem
  • processes
  • silos

Any organization must address these four elements in advance of (ideally) or in conjunction with the implementation of a new technology if that organization is going to realize success and sustainability….(More)”.

Charities are underestimating the importance of trust. That’s a problem.


Jill Halford & Neil Sherlock at NPC: “A growing mistrust and scepticism of organisations, experts and leaders has become a defining feature of recent times, causing many to question established truths that they’ve traditionally held dear. Against a backdrop of increasing volumes of data and commentary, amplified by social media, and the rise of ‘fake news’, it has become much harder for the public to both know who the experts are and to trust them to get things right. This directly impacts many charities who are themselves experts in their field and rely on the public to listen to and respond to their advice. In an increasingly digitalised world, there’s a sense that it is harder to gain and retain trust. There are growing concerns among CEOs about the impact of social media on the level of trust in their industry.

A growing mistrust and scepticism of organisations, experts and leaders has become a defining feature of recent times.

The questioning of experts is underpinned by a pervading sense that many actors are driven by hidden or ulterior motives, perhaps making some people less willing to trust organisations and their leaders. The Edelman Trust Barometer 2017 finds that 60% of the UK public think ‘the system’ is failing. This is defined as feeling a sense of injustice, a lack of hope and confidence and a desire for change. There is an emerging view that everyone from politicians, to businesses to charities need to do more to explain what they do and how it benefits both individuals and wider society….Public polling for the Charity Commission showed that the overall level of trust and confidence in charities fell from 6.7 out of 10 in 2012 and 2014 to 5.7 in 2016. This is a trend that is also reflected in the Edelman Trust Barometer 2017. Meanwhile other studies suggest that trust is bouncing back.

 

…Trust is often an overlooked asset for charities. For many organisations, trust can typically only come on the agenda when things are going wrong. NPC’s State of the Sector research report Charities taking charge shows that nearly a third of charity leaders think a loss of trust in the sector would have no effect on their organisation. The research also finds a narrow association between trust and fundraising rather than taking a more holistic view to trust.

Trust is a fundamental prerequisite of effective human interaction and meaningful, constructive relationships.

But trust matters deeply to people, and so it should matter to the organisations that serve them. Trust is considered a fundamental prerequisite of effective human interaction and meaningful, constructive relationships. It is the ‘glue’ that binds society and the economy together. There is a clear need for all organisations to take a broader view of trust. While those charities that rely on fundraising may feel that they need to be more concerned with public trust than a philanthropic foundation, for example, trust impacts a charity in many ways. For example, people’s trust in an organisation can fundamentally shape their behaviour and actions towards it. This can include trusting an organisation with your data and personal information, being more willing to collaborate and engage, and listening and acting on advice and expertise.

Trust is a powerful asset for organisations in four specific ways:

  • trust drives performance;
  • trust allows organisations to be true to themselves;
  • trust can help win round stakeholder scepticism; and
  • trust can put organisations on the front foot in a crisis that will inevitably happen at some point, positioning them in a better place to recover.

All four of these should resonate with charities as they seek to deliver greater impact in line with their values and ethos….(More).

We have unrealistic expectations of a tech-driven future utopia


Bob O’Donnell in RECODE: “No one likes to think about limits, especially in the tech industry, where the idea of putting constraints on almost anything is perceived as anathema.

In fact, the entire tech industry is arguably built on the concept of bursting through limitations and enabling things that weren’t possible before. New technology developments have clearly created incredible new capabilities and opportunities, and have generally helped improve the world around us.

But there does come a point — and I think we’ve arrived there — where it’s worth stepping back to both think about and talk about the potential value of, yes, technology limits … on several different levels.

On a technical level, we’ve reached a point where advances in computing applications like AI, or medical applications like gene splicing, are raising even more ethical questions than practical ones on issues such as how they work and for what applications they might be used. Not surprisingly, there aren’t any clear or easy answers to these questions, and it’s going to take a lot more time and thought to create frameworks or guidelines for both the appropriate and inappropriate uses of these potentially life-changing technologies.

Does this mean these kinds of technological advances should be stopped? Of course not. But having more discourse on the types of technologies that get created and released certainly needs to happen.

 Even on a practical level, the need for limiting people’s expectations about what a technology can or cannot do is becoming increasingly important. With science-fiction-like advances becoming daily occurrences, it’s easy to fall into the trap that there are no limits to what a given technology can do. As a result, people are increasingly willing to believe and accept almost any kind of statements or predictions about the future of many increasingly well-known technologies, from autonomous driving to VR to AI and machine learning. I hate to say it, but it’s the fake news of tech.

Just as we’ve seen the fallout from fake news on all sides of the political perspective, so, too, are we starting to see that unbridled and unlimited expectations for certain new technologies are starting to have negative implications of their own. Essentially, we’re starting to build unrealistic expectations for a tech-driven nirvana that doesn’t clearly jibe with the realities of the modern world, particularly in the time frames that are often discussed….(More)”.

How AI Is Crunching Big Data To Improve Healthcare Outcomes


PSFK: “The state of your health shouldn’t be a mystery, nor should patients or doctors have to wait long to find answers to pressing medical concerns. In PSFK’s Future of Health Report, we dig deep into the latest in AI, big data algorithms and IoT tools that are enabling a new, more comprehensive overview of patient data collection and analysis. Machine support, patient information from medical records and conversations with doctors are combined with the latest medical literature to help form a diagnosis without detracting from doctor-patient relations.

The impact of improved AI helps patients form a baseline for well-being and is making changes all across the healthcare industry. AI not only streamlines intake processes and reduces processing volume at clinics, it also controls input and diagnostic errors within a patient record, allowing doctors to focus on patient care and communication, rather than data entry. AI also improves pattern recognition and early diagnosis by learning from multiple patient data sets.

By utilizing deep learning algorithms and software, healthcare providers can connect various libraries of medical information and scan databases of medical records, spotting patterns that lead to more accurate detection and greater breadth of efficiency in medical diagnosis and research. IBM Watson, which has previously been used to help identify genetic markers and develop drugs, is applying its neural learning networks to help doctors correctly diagnose heart abnormalities from medical imaging tests. By scanning thousands of images and learning from correct diagnoses, Watson is able to increase diagnostic accuracy, supporting doctors’ cardiac assessments.

Outside of the doctor’s office, AI is also being used to monitor patient vitals to help create a baseline for well-being. By monitoring health on a day-to-day basis, AI systems can alert patients and medical teams to abnormalities or changes from the baseline in real time, increasing positive outcomes. Take xbird, a mobile platform that uses artificial intelligence to help diabetics understand when hypoglycemic attacks will occur. The AI combines personal and environmental data points from over 20 sensors within mobile and wearable devices to create an automated personal diary and cross references it against blood sugar levels. Patients then share this data with their doctors in order to uncover their unique hypoglycemic triggers and better manage their condition.

In China, meanwhile, web provider Baidu has debuted Melody, a chat-based medical assistant that helps individuals communicate their symptoms, learn of possible diagnoses and connect to medical experts….(More)”.