Article by Reid Blackman and Tamara Sipes: “New tools are enabling organizations to invite and leverage non-data scientists — say, domain data experts, team members very familiar with the business processes, or heads of various business units — to propel their AI efforts. There are advantages to empowering these internal “citizen data scientists,” but also risks. Organizations considering implementing these tools should take five steps: 1) provide ongoing education, 2) provide visibility into similar use cases throughout the organization, 3) create an expert mentor program, 4) have all projects verified by AI experts, and 5) provide resources for inspiration outside your organization…(More)”.
When do “Nudges” Increase Welfare?
Paper by Hunt Allcott, Daniel Cohen, William Morrison & Dmitry Taubinsky: “Policymakers are increasingly interested in non-standard policy instruments (NPIs), or “nudges,” such as simplified information disclosure and warning labels. We characterize the welfare effects of NPIs using public finance sufficient statistic approaches, allowing for endogenous prices, market power, and optimal or suboptimal taxes. While many empirical evaluations have focused on whether NPIs increase ostensibly beneficial behaviors on average, we show that this can be a poor guide to welfare. Welfare also depends on whether the NPI reduces the variance of distortions from heterogenous biases and externalities, and the average effect becomes irrelevant with zero pass-through or optimal taxes. We apply our framework to randomized experiments evaluating automotive fuel economy labels and sugary drink health labels. In both experiments, the labels increase ostensibly beneficial behaviors but also may decrease welfare in our model, because they increase the variance of distortions…(More)”.
Design-led policy and governance in practice: a global perspective
Paper by Marzia Mortati, Louise Mullagh & Scott Schmidt: “Presently, the relationship between policy and design is very much open for debate as to how these two concepts differ, relate, and interact with one another. There exists very little agreement on their relational trajectory with one course, policy design, originating in the policy studies tradition while the other, design for policy, being founded in design studies. The Special Issue has paid particular attention to the upcoming area of research where design disciplines and policy studies are exploring new ways toward convergence. With a focus on design, the authors herein present an array of design methods and approaches through case studies and conceptual papers, using co-design, participatory design and critical service design to work with policymakers in tackling challenging issues and policies. We see designers and policymakers working with communities to boost engagement around the world, with examples from the UK, Latvia, New Zealand, Denmark, Turkey, the UK, Brazil and South Africa. Finally, we offer a few reflections to build further this research area pointing out topics for further research with the hope that these will be relevant for researchers approaching the field or deepening their investigation and for bridging the academic/practice divide between design studies and policy design…(More)”.
Open Data in Europe 2022
European Commission: “A series of indicators have been selected to measure Open Data maturity across Europe. These indicators cover the level of development of national policies promoting Open Data, an assessment of the features made available on national data portals as well as the expected impact of Open Data….(More)”
The rise and fall of peer review
Blog by Adam Mastroianni: “For the last 60 years or so, science has been running an experiment on itself. The experimental design wasn’t great; there was no randomization and no control group. Nobody was in charge, exactly, and nobody was really taking consistent measurements. And yet it was the most massive experiment ever run, and it included every scientist on Earth.
Most of those folks didn’t even realize they were in an experiment. Many of them, including me, weren’t born when the experiment started. If we had noticed what was going on, maybe we would have demanded a basic level of scientific rigor. Maybe nobody objected because the hypothesis seemed so obviously true: science will be better off if we have someone check every paper and reject the ones that don’t pass muster. They called it “peer review.”
This was a massive change. From antiquity to modernity, scientists wrote letters and circulated monographs, and the main barriers stopping them from communicating their findings were the cost of paper, postage, or a printing press, or on rare occasions, the cost of a visit from the Catholic Church. Scientific journals appeared in the 1600s, but they operated more like magazines or newsletters, and their processes of picking articles ranged from “we print whatever we get” to “the editor asks his friend what he thinks” to “the whole society votes.” Sometimes journals couldn’t get enough papers to publish, so editors had to go around begging their friends to submit manuscripts, or fill the space themselves. Scientific publishing remained a hodgepodge for centuries.
(Only one of Einstein’s papers was ever peer-reviewed, by the way, and he was so surprised and upset that he published his paper in a different journal instead.)
That all changed after World War II. Governments poured funding into research, and they convened “peer reviewers” to ensure they weren’t wasting their money on foolish proposals. That funding turned into a deluge of papers, and journals that previously struggled to fill their pages now struggled to pick which articles to print. Reviewing papers before publication, which was “quite rare” until the 1960s, became much more common. Then it became universal.
Now pretty much every journal uses outside experts to vet papers, and papers that don’t please reviewers get rejected. You can still write to your friends about your findings, but hiring committees and grant agencies act as if the only science that exists is the stuff published in peer-reviewed journals. This is the grand experiment we’ve been running for six decades.
The results are in. It failed…(More)”.
A catalyst for community-wide action on sustainable development
Article by Communities around the world are increasingly recognizing that breaking down silos and leveraging shared resources and interdependencies across economic, social, and environmental issues can help accelerate progress on multiple issues simultaneously. As a framework for organizing local development priorities, the world’s 17 Sustainable Development Goals (SDGs) uniquely combine a need for broad technical expertise with an opportunity to synergize across domains—all while adhering to the principle of leaving no one behind. For local leaders attempting to tackle intersecting issues using the SDGs, one underpinning question is how to support new forms of collaboration to maximize impact and progress?
In early May, over 100 people across the East Central Florida (ECF) region in the U.S. participated in “Partnership for the Goals: Creating a Resilient and Thriving Community,” a two-day multi-stakeholder convening spearheaded by a team of local leaders from the East Central Florida Regional Resilience Collaborative (ECFR2C), the Central Florida Foundation, the City of Orlando, Florida for Good, Orange County, and the University of Central Florida. The convening grew out of a multi-year resilience planning process that leveraged the SDGs as a framework for tackling local economic, social, and environmental priorities all at once.
To move from community-wide planning to community-wide action, the organizers experimented with a 17 Rooms process—a new approach to accelerating collaborative action for the SDGs pioneered by the Center for Sustainable Development at Brookings and The Rockefeller Foundation. We collaborated with the ECF local organizing team and, in the process, spotted a range of more broadly relevant insights that we describe here…(More)”.
Navigating the Crisis: How Governments Used Intelligence for Decision Making During the COVID-19 Pandemic
Report by Geoff Mulgan, Oliver Marsh, and Anina Henggeler: “…examines how governments — and the societies around them — mobilised intelligence to handle the COVID-19 pandemic and its effects. It also makes recommendations as to how they could improve their ability to organise intelligence for future challenges of all kinds, from pandemics to climate change.
The study draws on dozens of interviews with senior officials and others in many countries including Estonia, Australia, New Zealand, Germany, Finland, USA, Chile, Canada, Portugal, Taiwan, Singapore, India, Bangladesh, UAE, South Korea and the UK, as well as the European Commission and UN agencies — along with roundtables and literature analysis.
The pandemic was an unprecedented event in its global impacts and in the scale of government responses. It required a myriad of policy decisions: about testing, lockdowns, masks, school closures, visiting rules at care homes and vaccinations.
Our interest is in what contributed to those decisions, and we define intelligence broadly to include data, evidence, models, tacit knowledge, foresight and creativity and innovation — all the means that can help governments make better decisions, particularly under conditions of stress and uncertainty.
Each type of intelligence played an important role. Governments needed health as well as non-health data to help understand how the virus was spreading in real time and its impacts. They needed models — for example, to judge if their hospitals were at risk of being overrun. They needed evidence — for example on whether enforcing mask-wearing would be effective. And they needed to tap into the knowledge of citizens and frontline staff quickly to spot potential problems and frictions.
Most governments had to improvise new methods of organising that intelligence, particularly as they grappled not just with the immediate health challenges, but also with the knock-on challenges to economies, communities, mental health, school systems and sectors such as hospitality.
As we show there was extraordinary innovation globally around the gathering of data, from mass serological testing to analysis of sewage, from mobilising mobile phone data to citizen generated data on symptoms. There was an equally impressive explosion of research and evidence; and innovative approaches to problem solving and creativity, from vaccine development to Personal Protective Equipment (PPE).
However, we also point to problems:
- Imbalances in terms of what was attended to — with physical health given much more attention than mental health or educational impacts in models and data, which was understandable in the early phases of the crisis but more problematic later on as trade-offs had to be managed
- Imbalances in different kinds of expertise in scientific advice and influence, for instance in who got to sit on and be heard in expert advisory committees
- Very varied ability of countries to share information and data between tiers of government
- Very varied ability to mobilise key sources, such as commercial data, and varied use of intelligence from outside sources, such as from other countries or from civic groups,
- Even when there were strong sources of advice and evidence, weak capacities to synthesise multiple kinds of intelligence at the core of governments…(More)”.
The Dangers of Systems Illiteracy
Review by Carol Dumaine: “In 1918, as the Great War was coming to an end after four bloody years of brutal conflict, an influenza pandemic began to ravage societies around the globe. While in Paris negotiating the terms of the peace agreement in the spring of 1919, evidence indicates that US president Woodrow Wilson was stricken with the flu.
Wilson, who had been intransigent in insisting on just peace terms for the defeated nations (what he called “peace without victory”), underwent a profound change of mental state that his personal physician and closest advisors attributed to his illness. While sick, Wilson suddenly agreed to all the terms he had previously adamantly rejected and approved a treaty that made onerous demands of Germany.
Wilson’s reversal left Germans embittered and his own advisors disillusioned. Historian John M. Barry, who recounts this episode in his book about the 1918 pandemic, The Great Influenza, observes that most historians agree “that the harshness toward Germany of the Paris peace treaty helped create the economic hardship, nationalistic reaction, and political chaos that fostered the rise of Hitler.”
This anecdote is a vivid illustration of how a public health disaster can intersect with world affairs, potentially sowing the seeds for a future of war. Converging crises can leave societies with too little time to regroup, breaking down resilience and capacities for governance. Barry concludes from his research into the 1918 pandemic that to forestall this loss of authority—and perhaps to avoid future, unforeseen repercussions—government leaders should share the unvarnished facts and evolving knowledge of a situation.
Society is ultimately based on trust; during the flu pandemic, “as trust broke down, people became alienated not only from those in authority, but from each other.” Barry continues, “Those in authority must retain the public’s trust. The way to do that is to distort nothing, to put the best face on nothing, to try to manipulate no one.”
Charles Weiss makes a similar argument in his new book, The Survival Nexus: Science, Technology, and World Affairs. Weiss contends that the preventable human and economic losses of the COVID-19 pandemic were the result of politicians avoiding harsh truths: “Political leaders suppressed evidence of virus spread, downplayed the importance of the epidemic and the need to observe measures to protect the health of the population, ignored the opinions of local experts, and publicized bogus ‘cures’—all to avoid economic damage and public panic, but equally importantly to consolidate political power and to show themselves as strong leaders who were firmly in control.” …(More)”.
Industry Data for Society Partnership
Press Release: “On Wednesday, a new Industry Data for Society Partnership (IDSP) was launched by GitHub, Hewlett Packard Enterprise (HPE), LinkedIn, Microsoft, Northumbrian Water Group, R2 Factory and UK Power Networks. The IDSP is a first-of-its-kind cross-industry partnership to help advance more open and accessible private-sector data for societal good. The founding members of the IDSP agree to provide greater access to their data, where appropriate, to help tackle some of the world’s most pressing challenges in areas such as sustainability and inclusive economic growth.
In the past few years, open data has played a critical role in enabling faster research and collaboration across industries and with the public sector. As we saw during COVID-19, pandemic data that was made more open enabled researchers to make faster progress and gave citizens more information to inform their day-to-day activities. The IDSP’s goal is to continue this model into new areas and help address other complex societal challenges. The IDSP will serve as a forum for the participating companies to foster collaboration, as well as a resource for other entities working on related issues.
IDSP members commit to the following:
- To open data or provide greater access to data, where appropriate, to help solve pressing societal problems in a usable, responsible and inclusive manner.
- To share knowledge and information for the effective use of open data and data collaboration for social benefit.
- To invest in skilling a broad class of professionals to use data effectively and responsibly for social impact.
- To protect individuals’ privacy in all these activities.
The IDSP will also bring in other organizations with expertise in societal issues. At launch, The GovLab’s Data Program based at New York University and the Open Data Institute will both be partnership Affiliates to provide guidance and expertise for partnership endeavors…(More)”.
Using private sector geospatial data to inform policy
OECD Report: “Over the last decade, a large variety of geospatial data sources, such as GPS trajectories, geotagged photos, and social media have become available for research and statistical applications. These new data sources are often generated, voluntarily or non-voluntarily, by private sector organisations and can provide highly granular and timely information to policymakers. Drawing on experiences of several OECD countries, this paper highlights the potential of combining traditional and unconventional data from both public and private sources, and makes the case for facilitating co-operation between data providers and organisations responsible for public policy. In addition, the paper provides a series of best practices on leveraging private data for the public good and identifies opportunities, challenges, and ways forward for public and private sector partnerships on data sharing….(More)”.