Paper by Josh Lerner: “Anti-democratic movements are surging around the world, threatening to undermine elections and replace them with oligarchy. Pro-democracy movements mainly focus on defending elections, even though most people think that elections alone are inadequate. While elections dominate current thinking about democracy, the history and future of democracy is much broader. For over 5,000 years, people have built up competing waves of electoral, direct, deliberative, and participatory democracy. We are now seeing a transition, however, from waves to ecosystems. Rather than seeking one single solution to our ailing democracy, a new generation of democracy reformers is weaving together different democratic practices into balanced democratic ecosystems. This white paper provides a roadmap for this emerging next stage of democratic innovation. It reviews the limitations of elections, the different waves of democratic innovation and efforts to connect them, and key challenges and strategies for building healthy ecosystems of democracy…(More)”.
AI, data governance and privacy
OECD Report: “Recent AI technological advances, particularly the rise of generative AI, have raised many data governance and privacy questions. However, AI and privacy policy communities often address these issues independently, with approaches that vary between jurisdictions and legal systems. These silos can generate misunderstandings, add complexities in regulatory compliance and enforcement, and prevent capitalising on commonalities between national frameworks. This report focuses on the privacy risks and opportunities stemming from recent AI developments. It maps the principles set in the OECD Privacy Guidelines to the OECD AI Principles, takes stock of national and regional initiatives, and suggests potential areas for collaboration. The report supports the implementation of the OECD Privacy Guidelines alongside the OECD AI Principles. By advocating for international co-operation, the report aims to guide the development of AI systems that respect and support privacy…(More)”.
Government + research + philanthropy: How cross-sector partnerships can improve policy decisions and action
Paper by Jenni Owen: “Researchers often lament that government decision-makers do not generate or use research evidence. People in government often lament that researchers are not responsive to government’s needs. Yet there is increasing enthusiasm in government, research, and philanthropy sectors for developing, investing in, and sustaining government-research partnerships that focus on government’s use of evidence. There is, however, scant guidance about how to do so. To help fill the gap, this essay addresses (1) Why government-research partnerships matter; (2) Barriers to developing government-research partnerships; (3) Strategies for addressing the barriers; (4) The role of philanthropy in government-research partnerships. The momentum to develop, invest in, and sustain cross-sector partnerships that advance government’s use of evidence is exciting. It is especially encouraging that there are feasible and actionable strategies for doing so…(More)”.
Oracles in the Machine
Essay by Zora Che: “…In sociologist Charles Cooley’s theory of the “looking glass of self,” we understand ourselves through the perceptions of others. Online, models perceive us, responding to and reinforcing the versions of ourselves which they glean from our behaviors. They sense my finger lingering, my invisible gaze apparent by the gap of my movements. My understanding of my digital self and my digital reality becomes a feedback loop churned by models I cannot see. Moreover, the model only “sees” me as data that can be optimized for objectives that I cannot uncover. That objective is something closer to optimizing my time spent on the digital product than to holding my deepest needs; the latter perhaps was never a mathematical question to begin with.
Divination and algorithmic opacity both appear to bring us what we cannot see. Diviners see through what is obscure and beyond our comprehension: it may be incomprehensible pain and grief, vertiginous lack of control, and/or the unwarranted future. The opacity of divination comes from the limitations of our own knowledge. But the opacity of algorithms comes from both the algorithm itself and the socio-technical infrastructure that it was built around. Jenna Burrell writes of three layers of opacity in models: “(1) opacity as intentional corporate or state secrecy, (2) opacity as technical illiteracy, and (3) an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully.” As consumers of models, we interact with the first and third layer of the opacity―that of platforms hiding models from us, and that of the gap between what the model is optimizing for and what may be explainable. The black-box model is an alluring oracle, interacting with us in inexplicable ways: no explanation for the daily laconic message Co-Star pushes to its users, no logic behind why you received this tarot reading while scrolling, no insight into the models behind these oracles and their objectives…(More)”.
How Philanthropy Can Make Sure Data Is Used to Help — Not Harm
Article by Ryan Merkley: “We are living in an extractive data economy. Every day, people generate a firehose of new data on hundreds of apps and services. These data are often sold by data brokers indiscriminately, embedded into user profiles for ad targeting, and used to train large language models such as Chat GPT. Communities and individuals should benefit from data made by and about them, but they don’t.
That needs to change. A report released last month by the Aspen Institute, where I work, calls on foundations and other donors to lead the way in addressing these disparities and promoting responsible uses of data in their own practices and in the work of grantees. Among other things, it suggests that funders encourage grantees to make sure their data accurately represents the communities they serve and support their efforts to make that data available and accessible to constituents…(More)”.
Unlocking the Potential of Data: Innovative Policies for Responsible Data Reuse and Addressing Data Asymmetries
Testimony by Stefaan Verhulst to the German Bundestag: “Let me begin by highlighting the potential of data when used and reused responsibly. Although we hear much about the risks of using data–and many of the fears are indeed justified–it’s also important to keep in mind the very real possibilities that data offers for advancing the public good.
We live in a datafied world, characterized by an unprecedented supply–even glut–of data. In this world, data has become a critical resource for informing policy and decision-making processes. When properly analyzed and utilized, data can play a critical role in helping policymakers–and other stakeholders–address a range of critical problems, in sectors as diverse as public health, climate, innovation and economic development, combating urban decay–and much more.
Sometimes this data is readily available. Most of the time it is not. One of the areas with the biggest potential–yet also significant challenges–is data reuse – data already collected for one purpose using it for another. Data reuse can provide invaluable insights into current phenomena, help us understand the causes of emerging trends, and guide us in developing effective solutions to pressing challenges. Moreover, analysis from data re-use can serve as a powerful tool for anticipating future developments and prescribing targeted interventions…
Despite the very potential of data and data reuse, it’s undeniable we face significant challenges in realizing data’s full societal value.
One of the primary obstacles is a lack of access to high-quality, timely data by the public sector, civil society, and other groups that are working toward the public good.
We live in a paradoxical situation today, marked both by the availability of an unprecedented amount of data, but also by unprecedented asymmetries in access to that data for reuse in the public interest.
I believe that the growing asymmetries between those who have data (often from the private sector) and those who are best positioned to use it for the public good, represents one of the major challenges of our era.
Data policy to date has primarily focused on preventing the misuse of data, often for valid reasons as mentioned earlier. However, this approach has inadvertently overlooked the missed uses of data – the opportunities we fail to capitalize on due to overly restrictive policies or lack of innovative frameworks for data sharing and utilization…
Given these challenges, what can policymakers do? What steps can policymakers such as yourselves – and other stakeholders, from the private sector, academia and civil society – take to help maximize the potential of our datafied society and economy, and to ensure that the benefits of our data age are maximized in as equitable and inclusive a manner as possible?..(More)” (German) (See also: Experten: Innovative Ansätze in der Datenpolitik nötig).
Taking [A]part: The Politics and Aesthetics of Participation in Experience-Centered Design
Book by John McCarthy and Peter Wright: “…consider a series of boundary-pushing research projects in human-computer interaction (HCI) in which the design of digital technology is used to inquire into participative experience. McCarthy and Wright view all of these projects—which range from the public and performative to the private and interpersonal—through the critical lens of participation. Taking participation, in all its variety, as the generative and critical concept allows them to examine the projects as a part of a coherent, responsive movement, allied with other emerging movements in DIY culture and participatory art. Their investigation leads them to rethink such traditional HCI categories as designer and user, maker and developer, researcher and participant, characterizing these relationships instead as mutually responsive and dialogical.
McCarthy and Wright explore four genres of participation—understanding the other, building relationships, belonging in community, and participating in publics—and they examine participatory projects that exemplify each genre. These include the Humanaquarium, a participatory musical performance; the Personhood project, in which a researcher and a couple explored the experience of living with dementia; the Prayer Companion project, which developed a technology to inform the prayer life of cloistered nuns; and the development of social media to support participatory publics in settings that range from reality game show fans to on-line deliberative democracies…(More)”
Illuminating Lived Experience
Lab Note from the Sydney Policy Lab: “The lived experiences of people involved in care – from informal and formal care workers to the people they support – is foundational to the Australia Cares project. To learn from the ways people with lived experience are included in co-design and research methods, the Sydney Policy Lab initiated reflective research that has resulted in a Lab Note on Illuminating Lived Experience (pdf, 1MB).
Through a series of interviews, dialogues and collaborative writing processes, co-authors explored tensions between different approaches and core concepts underpinning lived experience methods and shared examples of those methods in practice.
Illuminating Lived Experience poses questions that may help guide researchers and policymakers seeking to engage people with lived experience and three core principles we believe are required for such engagements.
The Lab Note aims to encourage researchers to be creative in the ways co-design and lived experience are approached while being true to the critical roots of participatory methodologies. Rather than prescribing methods, the principles and practices developed are offered as a guide – a starting point for play…(More)”
Real Chaos, Today! Are Randomized Controlled Trials a good way to do economics?
Article by Maia Mindel: “A few weeks back, there was much social media drama about this a paper titled: “Social Media and Job Market Success: A Field Experiment on Twitter” (2024) by Jingyi Qiu, Yan Chen, Alain Cohn, and Alvin Roth (recipient of the 2012 Nobel Prize in Economics). The study posted job market papers by economics PhDs, and then assigned prominent economists (who had volunteered) to randomly promote half of them on their profiles(more detail on this paper in a bit).
The “drama” in question was generally: “it is immoral to throw dice around on the most important aspect of a young economist’s career”, versus “no it’s not”. This, of course, awakened interest in a broader subject: Randomized Controlled Trials, or RCTs.
R.C.T. T.O. G.O.
Let’s go back to the 1600s – bloodletting was a common way to cure diseases. Did it work? Well, doctor Joan Baptista van Helmont had an idea: randomly divvy up a few hundred invalids into two groups, one of which got bloodletting applied, and another one that didn’t.
While it’s not clear this experiment ever happened, it sets up the basic principle of the randomized control trial: the idea here is that, to study the effects of a treatment, (in a medical context, a medicine; in an economics context, a policy), a sample group is divided between two: the control group, which does not receive any treatment, and the treatment group, which does. The modern randomized controlled (or control) trial has three “legs”: it’s randomized because who’s in each group gets chosen at random, it’s controlled because there’s a group that doesn’t get the treatment to serve as a counterfactual, and it’s a trial because you’re not developing “at scale” just yet.
Why could it be important to randomly select people for economic studies? Well, you want the only difference, on average, between the two groups to be whether or not they get the treatment. Consider military service: it’s regularly trotted out that drafting kids would reduce crime rates. Is this true? Well, the average person who is exempted from the draft could be, systematically, different than the average person who isn’t – for example, people who volunteer could be from wealthier families who are more patriotic, or poorer families who need certain benefits; or they could have physical disabilities that impede their labor market participation, or wealthier university students who get a deferral. But because many countries use lotteries to allocate draftees versus non draftees, you can get a group of people who are randomly assigned to the draft, and who on average should be similar enough to each other. One study in particular, about Argentina’s mandatory military service in pretty much all of the 20th century, finds that being conscripted raises the crime rate relative to people who didn’t get drafted through the lottery. This doesn’t mean that soldiers have higher crime rates than non soldiers, because of selection issues – but it does provide pretty good evidence that getting drafted is not good for your non-criminal prospects…(More)”.
The 4M Roadmap: A Higher Road to Profitability by Using Big Data for Social Good
Report by Brennan Lake: “As the private sector faces conflicting pressures to either embrace or shun socially responsible practices, companies with privately held big-data assets must decide whether to share access to their data for public good. While some managers object to data sharing over concerns of privacy and product cannibalization, others launch well intentioned yet short-lived CSR projects that fail to deliver on lofty goals.
By embedding Shared-Value principles into ‘Data-for-Good’ programs, data-rich firms can launch responsible data-sharing initiatives that minimize risk, deliver sustained impact, and improve overall competitiveness in the process.
The 4M Roadmap by Brennan Lake, a Big-Data and Social Impact professional, guides managers to adopt a ‘Data-for-Good’ model that emphasizes four key pillars of value-creation: Mission, Messaging, Methods, and Monetization. Through deep analysis and private-sector case studies, The 4M Roadmap demonstrates how companies can engage in responsible data sharing to benefit society and business alike…(More)”.