Handbook of Democratic Innovation and Governance

Curated on Posted on by Stefaan Verhulst

Book edited by Stephen Elstub and Oliver Escobar: “Democracies are currently undergoing a period of both challenge and renewal. Democratic innovations are proliferating in politics, governance, policy, and public administration. This Handbook of Democratic Innovation and Governance advances understanding of democratic innovations by critically reviewing their importance throughout the world. The overarching themes are a focus on citizens and their relationship to these innovations, and the resulting effects on political equality and policy impact.

The Handbook covers different types of democratic innovations; their potential to combat current problems with democracy; the various actors involved; their use in different areas of policy and governance; their application in different parts of the world; and the methods used to research them. Contributors therefore offer a definitive overview of existing research on democratic innovations, while also setting the agenda for future research and practice.

Featuring a critical combination of theoretical, empirical and methodological work on democratic innovations, this insightful Handbook balances depth, originality and accessibility to make it an ideal research companion for scholars and students of democratic governance alike. Public administrators and participation practitioners will also benefit from its guidance on citizen engagement processes….(More)”.

Innovation bureaucracies: How agile stability creates the entrepreneurial state

Curated on Posted on by Stefaan Verhulst

Paper by Rainer Kattel, Wolfgang Drechsler and Erkki Karo: “In this paper, we offer to redefine what entrepreneurial states are: these are states that are capable of unleashing innovations, and wealth resulting from those innovations, and of maintaining socio-political stability at the same time. Innovation bureaucracies are constellations of public organisations that deliver such agile stability. Such balancing acts make public bureaucracies unique in how they work, succeed and fail. The paper looks at the historical evolution of innovation bureaucracy by focusing on public organisations dealing with knowledge and technology, economic development and growth. We briefly show how agility and stability are delivered through starkly different bureaucratic organisations; hence, what matters for capacity and capabilities are not individual organisations, but organisational configurations and how they evolve….(More)”.

Accelerating Medicines Partnership (AMP): Improving Drug Research Efficiency through Biomarker Data Sharing

Curated on Posted on by Stefaan Verhulst

Data Collaborative Case Study by Michelle Winowatan, Andrew Young, and Stefaan Verhulst: “Accelerating Medicines Partnership (AMP) is a cross-sector data-sharing partnership in the United States between the National Institutes of Health (NIH), the Food and Drug Administration (FDA), multiple biopharmaceutical and life science companies, as well as non-profit organizations that seeks to improve the efficiency of developing new diagnostics and treatments for several types of disease. To achieve this goal, the partnership created a pre-competitive collaborative ecosystem where the biomedical community can pool data and resources that are relevant to the prioritized disease areas. A key component of the partnership is to make biomarkers data available to the medical research community through online portals.

Data Collaboratives Model: Based on our typology of data collaborative models, AMP is an example of the data pooling model of data collaboration, specifically a public data pool. Public data pools co-mingle data assets from multiple data holders — in this case pharmaceutical companies — and make those shared assets available on the web. Pools often limit contributions to approved partners (as public data pools are not crowdsourcing efforts), but access to the shared assets is open, enabling independent re-uses.

Data Stewardship Approach: Data stewardship is built into the partnership through the establishment of an executive committee, which governs the entire partnership, and a steering committee for each disease area, which governs each of the sub-projects within AMP. These committees consist of representatives from the institutional partners involved in AMP and perform data stewards function including enabling inter-institutional engagement as well as intra-institutional coordination, data audit and assessment of value and risk, communication of findings, and nurture the collaboration to sustainability….(Full Case Study)”.

The Economics of Violence: How Behavioral Science Can Transform our View of Crime, Insurgency, and Terrorism

Curated on Posted on by Stefaan Verhulst

Book by Gary M. Shiffman: “How do we understand illicit violence? Can we prevent it? Building on behavioral science and economics, this book begins with the idea that humans are more predictable than we like to believe, and this ability to model human behavior applies equally well to leaders of violent and coercive organizations as it does to everyday people. Humans ultimately seek survival for themselves and their communities in a world of competition. While the dynamics of ‘us vs. them’ are divisive, they also help us to survive. Access to increasingly larger markets, facilitated through digital communications and social media, creates more transnational opportunities for deception, coercion, and violence. If the economist’s perspective helps to explain violence, then it must also facilitate insights into promoting peace and security. If we can approach violence as behavioral scientists, then we can also better structure our institutions to create policies that make the world a more secure place, for us and for future generations….(More)”.

Open data for electricity modeling: Legal aspects

Curated on Posted on by Stefaan Verhulst

Paper by Lion Hirth: “Power system modeling is data intensive. In Europe, electricity system data is often available from sources such as statistical offices or system operators. However, it is often unclear if these data can be legally used for modeling, and in particular if such use infringes intellectual property rights. This article reviews the legal status of power system data, both as a guide for data users and for data publishers.

It is based on interpretation of the law, a review of the secondary literature, an analysis of the licenses used by major data distributors, expert interviews, and a series of workshops. A core finding is that in many cases the legality of current practices is doubtful: in fact, it seems likely that modelers infringe intellectual property rights quite regularly. This is true for industry analysis but also academic researchers. A straightforward solution is open data – the idea that data can be freely used, modified, and shared by anyone for any purpose. To be open, it is not sufficient for data to be accessible free of cost, it must also come with an open data license, the most common types of which are also reviewed in this paper….(More)”.

Missions: A beginner's guide

Curated on Posted on by Stefaan Verhulst

UCL Institute for Innovation and Public Purpose: “…The 21st century is becoming increasingly defined by the need to respond to major issues facing society, the environment around us and the possibility of developing a prosperous equal economy. Sometimes referred to as ‘grand challenges’, these include climate change, ageing societies, preventative healthcare, and generating sustainable growth for the benefit of all.

Innovation has not just a rate but also a direction. How that direction is set — not just by the government but by different actors and socio-political forces — is a key aspect of IIPP’s work. But how should we decide which direction? We use the concept of public value as a way to think about which direction innovation and industrial policy takes. Public value is value that is created collectively for a public purpose — this requires citizens to engage in defining purpose, nurturing capabilities and capacities, assess the value created, and ensure that societal value is distributed equitably…(More)”.

Federal Sources of Entrepreneurship Data: A Compendium

Curated on Posted on by Stefaan Verhulst

Compendium developed by Andrew Reamer: “The E.M. Kauffman Foundation has asked the George Washington Institute of Public Policy (GWIPP) to prepare a compendium of federal sources of data on self-employment, entrepreneurship, and small business development. The Foundation believes that the availability of useful, reliable federal data on these topics would enable robust descriptions and explanations of entrepreneurship trends in the United States and so help guide the development of effective entrepreneurship policies.


Achieving these ends first requires the identification and detailed description of available federal datasets, as provided in this compendium. Its contents include:

  • An overview and discussion of 18 datasets from four federal agencies, organized by two categories and five subcategories.
  • Tables providing information on each dataset, including:
    • scope of coverage of self-employed, entrepreneurs, and businesses;
    • data collection methods (nature of data source, periodicity, sampling frame, sample size);
    • dataset variables (owner characteristics, business characteristics and operations, geographic areas);
    • Data release schedule; and
    • Data access by format (including fixed tables, interactive tools, API, FTP download, public use microdata samples [PUMS], and confidential microdata).

For each dataset, examples of studies, if any, that use the data source to describe and explain trends in entrepreneurship.
The author’s aim is for the compendium to facilitate an assessment of the strengths and weaknesses of currently available federal datasets, discussion about how data availability and value can be improved, and implementation of desired improvements…(More)”

The cultural foundations of modern democracies

Curated on Posted on by Stefaan Verhulst

Damian J. Ruck, Luke J. Matthews, Thanos Kyritsis, Quentin D. Atkinson & R. Alexander Bentley at Nature Human Behavior: “National democracy is a rare thing in human history and its stability has long been tied to the cultural values of citizens. Yet it has not been established whether changing cultural values made modern democracy possible or whether those values were a response to democratic institutions. Here we combine longitudinal data and cohort information of nearly 500,000 individuals from 109 nations to track the co-evolution of democratic values and institutions over the last century.

We find that cultural values of openness towards diversity predict a shift towards democracy and that nations with low institutional confidence are prone to political instability. In addition, the presence of democratic institutions did not predict any substantive changes in the measured cultural values. These results hold accounting for other factors, including gross domestic product per capita and non-independence between nations due to shared cultural ancestry. Cultural values lead to, rather than follow, the emergence of democracy. This indicates that current stable democracies will be under threat, should cultural values of openness to diversity and institutional confidence substantially decline… (More).”

What is the Difference between a Conclusion and a Fact?

Curated on Posted on by Stefaan Verhulst

Paper by Howard M. Erichson: “In Ashcroft v. Iqbal, building on Bell Atlantic v. Twombly, the Supreme Court instructed district courts to treat a complaint’s conclusions differently from allegations of fact. Facts, but not conclusions, are assumed true for purposes of a motion to dismiss. The Court did little to help judges or lawyers understand the elusive distinction, and, indeed, obscured the distinction with its language. The Court said it was distinguishing “legal conclusions” from factual allegations. The application in Twombly and Iqbal, however, shows that the relevant distinction is not between law and fact, but rather between different types of factual assertions. This essay, written for a symposium on the tenth anniversary of Ashcroft v. Iqbal, explores the definitional problem with the conclusion-fact distinction and examines how district courts have applied the distinction in recent cases….(More)”.

Assessing employer intent when AI hiring tools are biased

Curated on Posted on by Stefaan Verhulst

Report by Caitlin Chin at Brookings: “When it comes to gender stereotypes in occupational roles, artificial intelligence (AI) has the potential to either mitigate historical bias or heighten it. In the case of the Word2vec model, AI appears to do both.

Word2vec is a publicly available algorithmic model built on millions of words scraped from online Google News articles, which computer scientists commonly use to analyze word associations. In 2016, Microsoft and Boston University researchers revealed that the model picked up gender stereotypes existing in online news sources—and furthermore, that these biased word associations were overwhelmingly job related. Upon discovering this problem, the researchers neutralized the biased word correlations in their specific algorithm, writing that “in a small way debiased word embeddings can hopefully contribute to reducing gender bias in society.”

Their study draws attention to a broader issue with artificial intelligence: Because algorithms often emulate the training datasets that they are built upon, biased input datasets could generate flawed outputs. Because many contemporary employers utilize predictive algorithms to scan resumes, direct targeted advertising, or even conduct face- or voice-recognition-based interviews, it is crucial to consider whether popular hiring tools might be susceptible to the same cultural biases that the researchers discovered in Word2vec.

In this paper, I discuss how hiring is a multi-layered and opaque process and how it will become more difficult to assess employer intent as recruitment processes move online. Because intent is a critical aspect of employment discrimination law, I ultimately suggest four ways upon which to include it in the discussion surrounding algorithmic bias….(More)”

This report from The Brookings Institution’s Artificial Intelligence and Emerging Technology (AIET) Initiative is part of “AI and Bias,” a series that explores ways to mitigate possible biases and create a pathway toward greater fairness in AI and emerging technologies.