Multiple Streams and Policy Ambiguity

Book by Rob A. DeLeo, Reimut Zohlnhöfer and Nikolaos Zahariadis: “The last decade has seen a proliferation of research bolstering the theoretical and methodological rigor of the Multiple Streams Framework (MSF), one of the most prolific theories of agenda-setting and policy change. This Element sets out to address some of the most prominent criticisms of the theory, including the lack of empirical research and the inconsistent operationalization of key concepts, by developing the first comprehensive guide for conducting MSF research. It begins by introducing the MSF, including key theoretical constructs and hypotheses. It then presents the most important theoretical extensions of the framework and articulates a series of best practices for operationalizing, measuring, and analyzing MSF concepts. It closes by exploring existing gaps in MSF research and articulating fruitful areas of future research…(More)”.

How Open-Source Software Empowers Nonprofits And The Global Communities They Serve

Article by Steve Francis: “One particular area where this challenge is evident is climate. Thousands of nonprofits strive to address the effects of a changing climate and its impact on communities worldwide. Headlines often go to big organizations doing high-profile work (planting trees, for instance) in well-known places. Money goes to large-scale commercial agriculture or new technologies — because that’s where profits are most easily made. But thousands of other communities of small farmers that aren’t as visible or profitable need help too. These communities come together to tackle a number of interrelated problems: climate, soil health and productivity, biodiversity and human health and welfare. They envision a more sustainable future.

The reality is that software is crafted to meet market needs, but these communities don’t represent a profitable market. Every major industry has its own software applications and a network of consultants to tune that software for optimal performance. A farm cooperative in less developed parts of the world seeking to maximize value for sustainably harvested produce faces very different challenges than do any of these business users. Often they need to collect and manipulate data in the field, on whatever mobile device they have, with little or no connectivity. Modern software systems are rarely designed to operate in such an environment; they assume the latest devices and continuous connectivity…(More)”.

Routledge Handbook of Risk, Crisis, and Disaster Communication

Book edited by Brooke Fisher Liu, and Amisha M. Mehta: “With contributions from leading academic experts and practitioners from diverse disciplinary backgrounds including communication, disaster, and health, this Handbook offers a valuable synthesis of current knowledge and future directions for the field. It is divided into four parts. Part One begins with an introduction to foundational theories and pedagogies for risk and crisis communication. Part Two elucidates knowledge and gaps in communicating about climate and weather, focusing on community and corporate positions and considering text and visual communication with examples from the US and Australia. Part Three provides insights on communicating ongoing and novel risks, crises, and disasters from US and European perspectives, which cover how to define new risks and translate theories and methodologies so that their study can support important ongoing research and practice. Part Four delves into communicating with diverse publics and audiences with authors examining community, first responder, and employee perspectives within developed and developing countries to enhance our understanding and inspire ongoing research that is contextual, nuanced, and impactful. Offering innovative insights into ongoing and new topics, this handbook explores how the field of risk, crisis, and disaster communications can benefit from theory, technology, and practice…(More)”

Building a trauma-informed algorithmic assessment toolkit

Report by Suvradip Maitra, Lyndal Sleep, Suzanna Fay, Paul Henman: “Artificial intelligence (AI) and automated processes provide considerable promise to enhance human wellbeing by fully automating or co-producing services with human service providers. Concurrently, if not well considered, automation also provides ways in which to generate harms at scale and speed. To address this challenge, much discussion to date has focused on principles of ethical AI and accountable algorithms with a groundswell of early work seeking to translate these into practical frameworks and processes to ensure such principles are enacted. AI risk assessment frameworks to detect and evaluate possible harms is one dominant approach, as are a growing body of AI audit frameworks, with concomitant emerging governmental and organisational regulatory settings, and associate professionals.

The research outlined in this report took a different approach. Building on work in social services on trauma-informed practice, researchers identified key principles and a practical framework that framed AI design, development and deployment as a reflective, constructive exercise that resulting in algorithmic supported services to be cognisant and inclusive of the diversity of human experience, and particularly those who have experienced trauma. This study resulted in a practical, co-designed, piloted Trauma Informed Algorithmic Assessment Toolkit.

This Toolkit has been designed to assist organisations in their use of automation in service delivery at any stage of their automation journey: ideation; design; development; piloting; deployment or evaluation. While of particular use for social service organisations working with people who may have experienced past trauma, the tool will be beneficial for any organisation wanting to ensure safe, responsible and ethical use of automation and AI…(More)”.

Predicting hotspots of unsheltered homelessness using geospatial administrative data and volunteered geographic information

Paper by Jessie Chien, Benjamin F. Henwood, Patricia St. Clair, Stephanie Kwack, and Randall Kuhn: “Unsheltered homelessness is an increasingly prevalent phenomenon in major cities that is associated with adverse health and mortality outcomes. This creates a need for spatial estimates of population denominators for resource allocation and epidemiological studies. Gaps in the timeliness, coverage, and spatial specificity of official Point-in-Time Counts of unsheltered homelessness suggest a role for geospatial data from alternative sources to provide interim, neighborhood-level estimates of counts and trends. We use citizen-generated data from homeless-related 311 requests, provider-based administrative data from homeless street outreach cases, and expert reports of unsheltered count to predict count and emerging hotspots of unsheltered homelessness in census tracts across the City of Los Angeles for 2019 and 2020. Our study shows that alternative data sources can contribute timely insights into the state of unsheltered homelessness throughout the year and inform the delivery of interventions to this vulnerable population…(More)”.

Applying Social and Behavioral Science to Federal Policies and Programs to Deliver Better Outcomes

The White House: “Human behavior is a key component of every major national and global challenge. Social and behavioral science examines if, when, and how people’s actions and interactions influence decisions and outcomes. Understanding human behavior through social and behavioral science is vitally important for creating federal policies and programs that open opportunities for everyone.

Today, the Biden-Harris Administration shares the Blueprint for the Use of Social and Behavioral Science to Advance Evidence-Based Policymaking. This blueprint recommends actions for agencies across the federal government to effectively leverage social and behavioral science in improving policymaking to deliver better outcomes and opportunities for people all across America. These recommendations include specific actions for agencies, such as considering social and behavioral insights early in policy or program development. The blueprint also lays out broader opportunities for agencies, such as ensuring agencies have a sufficient number of staff with social and behavioral science expertise.  

The blueprint includes nearly a hundred examples of how social and behavioral science is already used to make real progress on our highest priorities, including promoting safe, equitable, and engaged communities; protecting the environment and promoting climate innovation; advancing economic prosperity and the future of the workforce; enhancing the health outcomes of all Americans; rebuilding our infrastructure and building for tomorrow; and promoting national defense and international security. Social and behavioral science informs the conceptualization, development, implementation, dissemination, and evaluation of interventions, programs, and policies. Policymakers and social scientists can examine data about how government services reach people or measure the effectiveness of a program in assisting a particular community. Using this information, we can understand why programs sometimes fall short in delivering their intended benefits or why other programs are highly successful in delivering benefits. These approaches also help us design better policies and scale proven successful interventions to benefit the entire country…(More)”.

Data governance for the ecological transition: An infrastructure perspective

Article by Charlotte Ducuing: “This article uses infrastructure studies to provide a critical analysis of the European Union’s (EU) ambition to regulate data for the ecological transition. The EU’s regulatory project implicitly qualifies data as an infrastructure for a better economy and society. However, current EU law does not draw all the logical consequences derived from this qualification of data as infrastructure, which is one main reason why EU data legislation for the ecological transition may not deliver on its high political expectations. The ecological transition does not play a significant normative role in EU data legislation and is largely overlooked in the data governance literature. By drawing inferences from the qualification of data as an infrastructure more consistently, the article opens avenues for data governance that centre the ecological transition as a normative goal…(More)”.

May Contain Lies: How Stories, Statistics, and Studies Exploit Our Biases

Book by Alex Edmans: “Our lives are minefields of misinformation. It ripples through our social media feeds, our daily headlines, and the pronouncements of politicians, executives, and authors. Stories, statistics, and studies are everywhere, allowing people to find evidence to support whatever position they want. Many of these sources are flawed, yet by playing on our emotions and preying on our biases, they can gain widespread acceptance, warp our views, and distort our decisions.

In this eye-opening book, renowned economist Alex Edmans teaches us how to separate fact from fiction. Using colorful examples—from a wellness guru’s tragic but fabricated backstory to the blunders that led to the Deepwater Horizon disaster to the diet that ensnared millions yet hastened its founder’s death—Edmans highlights the biases that cause us to mistake statements for facts, facts for data, data for evidence, and evidence for proof.

Armed with the knowledge of what to guard against, he then provides a practical guide to combat this tide of misinformation. Going beyond simply checking the facts and explaining individual statistics, Edmans explores the relationships between statistics—the science of cause and effect—ultimately training us to think smarter, sharper, and more critically. May Contain Lies is an essential read for anyone who wants to make better sense of the world and better decisions…(More)”.

Empowered Mini-Publics: A Shortcut or Democratically Legitimate?

Paper by Shao Ming Lee: “Contemporary mini-publics involve randomly selected citizens deliberating and eventually tackling thorny issues. Yet, the usage of mini-publics in creating public policy has come under criticism, of which a more persuasive  strand  is  elucidated  by  eminent  philosopher  Cristina  Lafont,  who  argues  that  mini-publics  with  binding  decision-making  powers  (or  ‘empowered  mini-publics’)  are  an  undemocratic  ‘shortcut’  and  deliberative democrats thus cannot use empowered mini-publics for shaping public policies. This paper aims to serve as a nuanced defense of empowered mini-publics against Lafont’s claims. I argue against her  claims  by  explicating  how  participants  of  an  empowered  mini-public  remain  ordinary,  accountable,  and therefore connected to the broader public in a democratically legitimate manner. I further critique Lafont’s own proposals for non-empowered mini-publics and judicial review as failing to satisfy her own criteria for democratic legitimacy in a self-defeating manner and relying on a double standard. In doing so, I show how empowered mini-publics are not only democratic but can thus serve to expand democratic deliberation—a goal Lafont shares but relegates to non-empowered mini-publics…(More)”.

AI for social good: Improving lives and protecting the planet

McKinsey Report: “…Challenges in scaling AI for social-good initiatives are persistent and tough. Seventy-two percent of the respondents to our expert survey observed that most efforts to deploy AI for social good to date have focused on research and innovation rather than adoption and scaling. Fifty-five percent of grants for AI research and deployment across the SDGs are $250,000 or smaller, which is consistent with a focus on targeted research or smaller-scale deployment, rather than large-scale expansion. Aside from funding, the biggest barriers to scaling AI continue to be data availability, accessibility, and quality; AI talent availability and accessibility; organizational receptiveness; and change management. More on these topics can be found in the full report.

While overcoming these challenges, organizations should also be aware of strategies to address the range of risks, including inaccurate outputs, biases embedded in the underlying training data, the potential for large-scale misinformation, and malicious influence on politics and personal well-being. As we have noted in multiple recent articles, AI tools and techniques can be misused, even if the tools were originally designed for social good. Experts identified the top risks as impaired fairness, malicious use, and privacy and security concerns, followed by explainability (Exhibit 2). Respondents from not-for-profits expressed relatively more concern about misinformation, talent issues such as job displacement, and effects of AI on economic stability compared with their counterparts at for-profits, who were more often concerned with IP infringement…(More)”