Paper by Federico Bartolomucci & Francesco Leoni: “Data Collaboratives have gained traction as interorganizational partnerships centered on data exchange. They enhance the collective capacity of responding to contemporary societal challenges using data, while also providing participating organizations with innovation capabilities and reputational benefits. Unfortunately, data collaboratives often fail to advance beyond the pilot stage and are therefore limited in their capacity to deliver systemic change. The governance setting adopted by a data collaborative affects how it acts over the short and long term. We present a governance design model to develop context-dependent data collaboratives. Practitioners can use the proposed model and list of key reflective questions to evaluate the critical aspects of designing a governance model for their data collaboratives…(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)”.
A New National Purpose: Harnessing Data for Health
Report by the Tony Blair Institute: “We are at a pivotal moment where the convergence of large health and biomedical data sets, artificial intelligence and advances in biotechnology is set to revolutionise health care, drive economic growth and improve the lives of citizens. And the UK has strengths in all three areas. The immense potential of the UK’s health-data assets, from the NHS to biobanks and genomics initiatives, can unlock new diagnostics and treatments, deliver better and more personalised care, prevent disease and ultimately help people live longer, healthier lives.
However, realising this potential is not without its challenges. The complex and fragmented nature of the current health-data landscape, coupled with legitimate concerns around privacy and public trust, has made for slow progress. The UK has had a tendency to provide short-term funding across multiple initiatives, which has led to an array of individual projects – many of which have struggled to achieve long-term sustainability and deliver tangible benefits to patients.
To overcome these challenges, it will be necessary to be bold and imaginative. We must look for ways to leverage the unique strengths of the NHS, such as its nationwide reach and cradle-to-grave data coverage, to create a health-data ecosystem that is much more than the sum of its many parts. This will require us to think differently about how we collect, manage and utilise health data, and to create new partnerships and models of collaboration that break down traditional silos and barriers. It will mean treating data as a key health resource and managing it accordingly.
One model to do this is the proposed sovereign National Data Trust (NDT) – an endeavour to streamline access to and curation of the UK’s valuable health-data assets…(More)”.
On the Meaning of Community Consent in a Biorepository Context
Article by Astha Kapoor, Samuel Moore, and Megan Doerr: “Biorepositories, vital for medical research, collect and store human biological samples and associated data for future use. However, our reliance solely on the individual consent of data contributors for biorepository data governance is becoming inadequate. Big data analysis focuses on large-scale behaviors and patterns, shifting focus from singular data points to identifying data “journeys” relevant to a collective. The individual becomes a small part of the analysis, with the harms and benefits emanating from the data occurring at an aggregated level.
Community refers to a particular qualitative aspect of a group of people that is not well captured by quantitative measures in biorepositories. This is not an excuse to dodge the question of how to account for communities in a biorepository context; rather, it shows that a framework is needed for defining different types of community that may be approached from a biorepository perspective.
Engaging with communities in biorepository governance presents several challenges. Moving away from a purely individualized understanding of governance towards a more collectivizing approach necessitates an appreciation of the messiness of group identity, its ephemerality, and the conflicts entailed therein. So while community implies a certain degree of homogeneity (i.e., that all members of a community share something in common), it is important to understand that people can simultaneously consider themselves a member of a community while disagreeing with many of its members, the values the community holds, or the positions for which it advocates. The complex nature of community participation therefore requires proper treatment for it to be useful in a biorepository governance context…(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)”.
We don’t need an AI manifesto — we need a constitution
Article by Vivienne Ming: “Loans drive economic mobility in America, even as they’ve been a historically powerful tool for discrimination. I’ve worked on multiple projects to reduce that bias using AI. What I learnt, however, is that even if an algorithm works exactly as intended, it is still solely designed to optimise the financial returns to the lender who paid for it. The loan application process is already impenetrable to most, and now your hopes for home ownership or small business funding are dying in a 50-millisecond computation…
In law, the right to a lawyer and judicial review are a constitutional guarantee in the US and an established civil right throughout much of the world. These are the foundations of your civil liberties. When algorithms act as an expert witness, testifying against you but immune to cross examination, these rights are not simply eroded — they cease to exist.
People aren’t perfect. Neither ethics training for AI engineers nor legislation by woefully uninformed politicians can change that simple truth. I don’t need to assume that Big Tech chief executives are bad actors or that large companies are malevolent to understand that what is in their self-interest is not always in mine. The framers of the US Constitution recognised this simple truth and sought to leverage human nature for a greater good. The Constitution didn’t simply assume people would always act towards that greater good. Instead it defined a dynamic mechanism — self-interest and the balance of power — that would force compromise and good governance. Its vision of treating people as real actors rather than better angels produced one of the greatest frameworks for governance in history.
Imagine you were offered an AI-powered test for post-partum depression. My company developed that very test and it has the power to change your life, but you may choose not to use it for fear that we might sell the results to data brokers or activist politicians. You have a right to our AI acting solely for your health. It was for this reason I founded an independent non-profit, The Human Trust, that holds all of the data and runs all of the algorithms with sole fiduciary responsibility to you. No mother should have to choose between a life-saving medical test and her civil rights…(More)”.
Establish Data Collaboratives To Foster Meaningful Public Involvement
Article by Gwen Ottinger: “Federal agencies are striving to expand the role of the public, including members of marginalized communities, in developing regulatory policy. At the same time, agencies are considering how to mobilize data of increasing size and complexity to ensure that policies are equitable and evidence-based. However, community engagement has rarely been extended to the process of examining and interpreting data. This is a missed opportunity: community members can offer critical context to quantitative data, ground-truth data analyses, and suggest ways of looking at data that could inform policy responses to pressing problems in their lives. Realizing this opportunity requires a structure for public participation in which community members can expect both support from agency staff in accessing and understanding data and genuine openness to new perspectives on quantitative analysis.
To deepen community involvement in developing evidence-based policy, federal agencies should form Data Collaboratives in which staff and members of the public engage in mutual learning about available datasets and their affordances for clarifying policy problems…(More)”.
Repository of 80+ real-life examples of how to anticipate migration using innovative forecast and foresight methods is now LIVE!
BD4M Announcement: “Today, we are excited to launch the Big Data For Migration Alliance (BD4M) Repository of Use Cases for Anticipating Migration Policy! The repository is a curated collection of real-world applications of anticipatory methods in migration policy. Here, policymakers, researchers, and practitioners can find a wealth of examples demonstrating how foresight, forecast and other anticipatory approaches are applied to anticipating migration for policy making.
Migration policy is a multifaceted and constantly evolving field, shaped by a wide variety of factors such as economic conditions, geopolitical shifts or climate emergencies. Anticipatory methods are essential to help policymakers proactively respond to emerging trends and potential challenges. By using anticipatory tools, migration policy makers can draw from both quantitative and qualitative data to obtain valuable insights for their specific goals. The Big Data for Migration Alliance — a join effort of The GovLab, the International Organization for Migration and the European Union Joint Research Centre that seeks to improve the evidence base on migration and human mobility — recognizes the importance of the role of anticipatory tools and has worked on the creation of a repository of use cases that showcases the current use landscape of anticipatory tools in migration policy making around the world. This repository aims to provide policymakers, researchers and practitioners with applied examples that can inform their strategies and ultimately contribute to the improvement of migration policies around the world.
As part of our work on exploring innovative anticipatory methods for migration policy, throughout the year we have published a Blog Series that delved into various aspects of the use of anticipatory methods, exploring their value and challenges, proposing a taxonomy, and exploring practical applications…(More)”.
Potential competition impacts from the data asymmetry between Big Tech firms and firms in financial services
Report by the UK Financial Conduct Authority: “Big Tech firms in the UK and around the world have been, and continue to be, under active scrutiny by competition and regulatory authorities. This is because some of these large technology firms may have both the ability and the incentive to shape digital markets by protecting existing market power and extending it into new markets.
Concentration in some digital markets, and Big Tech firms’ key role, has been widely discussed, including in our DP22/05. This reflects both the characteristics of digital markets and the characteristics and behaviours of Big Tech firms themselves. Although Big Tech firms have different business models, common characteristics include their global scale and access to a large installed user base, rich data about their users, advanced data analytics and technology, influence over decision making and defaults, ecosystems of complementary products and strategic behaviours, including acquisition strategies.
Through our work, we aim to mitigate the risk of competition in retail financial markets evolving in a way that results in some Big Tech firms gaining entrenched market power, as seen in other sectors and jurisdictions, while enabling the potential competition benefits that come from Big Tech firms providing challenge to incumbent financial services firms…(More)”.
Using Artificial Intelligence to Map the Earth’s Forests
Article from Meta and World Resources Institute: “Forests harbor most of Earth’s terrestrial biodiversity and play a critical role in the uptake of carbon dioxide from the atmosphere. Ecosystem services provided by forests underpin an essential defense against the climate and biodiversity crises. However, critical gaps remain in the scientific understanding of the structure and extent of global forests. Because the vast majority of existing data on global forests is derived from low to medium resolution satellite imagery (10 or 30 meters), there is a gap in the scientific understanding of dynamic and more dispersed forest systems such as agroforestry, drylands forests, and alpine forests, which together constitute more than a third of the world’s forests.
Today, Meta and World Resources Institute are launching a global map of tree canopy height at a 1-meter resolution, allowing the detection of single trees at a global scale. In an effort to advance open source forest monitoring, all canopy height data and artificial intelligence models are free and publicly available…(More)”.