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Stefaan Verhulst

Neil Levy at the Journal of Medical Ethics: “Nudges—policy proposals informed by work in behavioural economics and psychology that are designed to lead to better decision-making or better behaviour—are controversial. Critics allege that they bypass our deliberative capacities, thereby undermining autonomy and responsible agency. In this paper, I identify a kind of nudge I call a nudge to reason, which make us more responsive to genuine evidence. I argue that at least some nudges to reason do not bypass our deliberative capacities. Instead, use of these nudges should be seen as appeals to mechanisms partially constitutive of these capacities, and therefore as benign (so far as autonomy and responsible agency are concerned). I sketch some concrete proposals for nudges to reason which are especially important given the apparent widespread resistance to evidence seen in recent political events….(More)”.

Nudges in a post-truth world

Book by Dietmar Offenhuber: “Waste is material information. Landfills are detailed records of everyday consumption and behavior; much of what we know about the distant past we know from discarded objects unearthed by archaeologists and interpreted by historians. And yet the systems and infrastructures that process our waste often remain opaque. In this book, Dietmar Offenhuber examines waste from the perspective of information, considering emerging practices and technologies for making waste systems legible and how the resulting datasets and visualizations shape infrastructure governance. He does so by looking at three waste tracking and participatory sensing projects in Seattle, São Paulo, and Boston.

Offenhuber expands the notion of urban legibility—the idea that the city can be read like a text—to introduce the concept of infrastructure legibility. He argues that infrastructure governance is enacted through representations of the infrastructural system, and that these representations stem from the different stakeholders’ interests, which drive their efforts to make the system legible. The Trash Track project in Seattle used sensor technology to map discarded items through the waste and recycling systems; the Forager project looked at the informal organization processes of waste pickers working for Brazilian recycling cooperatives; and mobile systems designed by the city of Boston allowed residents to report such infrastructure failures as potholes and garbage spills. Through these case studies, Offenhuber outlines an emerging paradigm of infrastructure governance based on a complex negotiation among users, technology, and the city….(More)”.

Waste Is Information

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)”.

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

 

Isabelle Liotard and Valérie Revest in Technological Forecasting and Social Change: “An increase of the innovation contests and their associated prizes have been observed since the 90s especially in the US through the sponsorship of the American Federal Agencies. The purpose of this article is to shed light on some of the direct and indirect effects of US federal agency contests not only on economic dynamics but also on social dynamics. Based on recent case studies, this paper describes the various positive impacts that federal agency contests may have: i) contests may display a strong incentive effect ex-ante and during the contest; ii) they may produce favourable spillovers after the contests, at innovation and economic levels in specified economic/industry sectors and iii) they may also play a beneficial social role, contributing to citizens’ education and awareness. Nevertheless, as a contest remains a sophisticated device, public decision makers must comply with certain requirements if they wish to benefit from this particular policy tool in order to spur innovation….(More)”

 

Contests as innovation policy instruments: Lessons from the US federal agencies’ experience

 at the Sociological Review: “Hydraulic fracturing, or fracking, is an emerging and growing industry that is having considerable effects on environments and health. Yet fracking often lacks environmental regulations that might be understood as governmental forms of care. In some locations in the US, citizens have taken up environmental monitoring as a way to address this perceived absence of care, and to evidence harm in order to argue for new infrastructures of care. This article documents the practices of residents engaged in monitoring air pollution near fracking sites in the US, as well as the participatory and practice-based research undertaken by the Citizen Sense research project to develop monitoring kits for residents to use and test over a period of seven months. Citizen sensing practices for monitoring air pollution can constitute ways of expressing care about environments, communities and individual and public health. Yet practices for documenting and evidencing harm through the ongoing collection of air pollution data are also speculative attempts to make relevant these unrecognised and overlooked considerations of the need for care. Working with the concept of speculation, this article advances alternative notions of evidence, care and policy that attend to citizens’ experiences of living in the gas fields. How do citizen sensing practices work towards alternative ways of evidencing harm? In what ways does monitoring with environmental sensors facilitate this process? And what new speculative practices emerge to challenge the uses of environmental sensors, as well as to expand the types of data gathered, along with their political impact?…(More)”.

Citizen sensing, air pollution and fracking: From ‘caring about your air’ to speculative practices of evidencing harm

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).
Government innovations and the hype cycle

“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)”

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

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)”

Global innovations in measurement and evaluation

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)”.

Digital transformation’s people problem

Johnson PA, Sieber RE, Scassa T, Stephens M, Robinson PJ. in Transactions in GIS: “The provision of open data by governments at all levels has rapidly increased over recent years. Given that one of the dominant motivations for the provision of open data is to generate ‘value’, both economic and civic, there are valid concerns over the costs incurred in this pursuit. Typically, costs of open data are framed as costs that are internal to the data providing government. Building on the strong history of GIScience research on data provision via spatial data infrastructures, this paper considers both the direct and indirect costs of open data provision, framing four main areas of indirect costs; citizen participation challenges, uneven provision across geography and user types, subsidy of private sector activities, and the creation of inroads for corporate influence on government. These areas of indirect cost lead to the development of critical questions, including constituency, purpose, enablement, protection, and priorities. These questions are proposed as a guide to governments that provide open data in addressing the indirect costs of open data….(More)”.

The Cost(s) of Open Geospatial Data

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