Stefaan Verhulst
A report by SDSN TReNDS and DataReady Limited on behalf of Contracts4DataCollaboration: “Building upon issues discussed in the C4DC report, “Laying the Foundation for Effective Partnerships: An Examination of Data Sharing Agreements,” this brief examines the potential of sunset clauses or sunset provisions to be a legally binding, enforceable, and accountable way of ensuring COVID-19 related data sharing agreements are wound down responsibly at the end of the pandemic. The brief is divided into four substantive parts: Part I introduces sunset clauses as legislative tools, highlighting a number of examples of how they have been used in both COVID-19 related and other contexts; Part II discusses sunset provisions in the context of data sharing agreements and attempts to explain the complex interrelationship between data ownership, intellectual property, and sunset provisions; Part III identifies some key issues policymakers should consider when assessing the utility and viability of sunset provisions within their data sharing agreements and arrangements; and Part IV highlights the value of a memorandum of understanding (MoU) as a viable vehicle for sunset provisions in contexts where data sharing agreements are either non-existent or not regularly used….(More)“.(Contracts 4 Data Collaboration Framework).
Phil Klay at the New York Times: “…Stories are a quintessentially human method of responding to the chaos and uncertainty of the world. Science is a quintessentially human method of trying to control that chaos, and data is its raw material. Adrift in the world, uncertain of the future, hostage to fate, but possessed of increasingly powerful tools for carving up pieces of the world and putting them under the microscope, is it any wonder that we increasingly turn to science when looking for deliverance from our human predicaments?
Science, after all, will eventually bring us to the end of the pandemic, just as it has helped limit the damage through better treatments and proof of the benefits of wearing masks. “Science over fiction,” was one slogan of the Joe Biden campaign, a welcome message to those who’d like public policy tethered more to reality than political fantasy.
But because science supposedly gives clear answers about everything from how to open schools in a pandemic to who will be elected president, we tend to rush to embrace it as a panacea. Some, like the popular podcaster and author Sam Harris, even think science can answer moral questions. Rarely does it occur to us how often the invocation of “science” is used to mask value judgments, or political deliberation.
When the Centers for Disease Control and Prevention and the American Academy of Pediatricians released separate guidelines for reopening schools, the difference lay not in the underlying science but in their institutional priorities, one focused on disease spread and the other on the welfare of children. Likewise, the difference in how New York City handled the reopenings of day cares and schools reflected not simply science, but also what could be more easily demanded of workers who lacked the protection of a powerful union.
As much as we’d like to believe in “science over fiction,” decisions in the real world require negotiating between what we think the data means, what human value we’d like to assign to it and what stories about it we can get others to accept. Data alone is not knowledge, and it is certainly not wisdom. It rarely says as much as we think it does.
Yet its allure is undeniable, persistent. As I watched the election returns on Tuesday and Wednesday, I did so with the sinking feeling that I’d been fooled again by the lure of data. Even though it looked like Biden could still win, it was clear that those hard numbers I’d been absorbing for weeks, based on fine -tuned methodologies, correcting for past mistakes, aggregated to minimize chances of error, hadn’t come close to reflecting reality. “You are literally working on an essay about the problems with relying too much on data,” my wife told me the morning after the election, “and yet you were so confident in the polls.”…(More)”
NIH Blogpost by Carrie Wolinetz: “Today, nearly twenty years after the publication of the Final NIH Statement on Sharing Research Data in 2003, we have released a Final NIH Policy for Data Management and Sharing. This represents the agency’s continued commitment to share and make broadly available the results of publicly funded biomedical research. We hope it will be a critical step in moving towards a culture change, in which data management and sharing is seen as integral to the conduct of research. Responsible data management and sharing is good for science; it maximizes availability of data to the best and brightest minds, underlies reproducibility, honors the participation of human participants by ensuring their data is both protected and fully utilized, and provides an element of transparency to ensure public trust and accountability.
This policy has been years in the making and has benefited enormously from feedback and input from stakeholders throughout the process. We are grateful to all those who took the time to comment on Request for Information, the Draft policy, or to participate in workshops or Tribal consultations. That thoughtful feedback has helped shape the Final policy, which we believe strikes a balance between reasonable expectations for data sharing and flexibility to allow for a diversity of data types and circumstances. How we incorporated public comments and decision points that led to the Final policy are detailed in the Preamble to the DMS policy.
The Final policy applies to all research funded or conducted by NIH that results in the generation of scientific data. The Final Policy has two main requirements (1) the submission of a Data Management and Sharing Plan (Plan); and (2) compliance with the approved Plan. We are asking for Plans at the time of submission of the application, because we believe planning and budgeting for data management and sharing needs to occur hand in hand with planning the research itself. NIH recognizes that science evolves throughout the research process, which is why we have built in the ability to update DMS Plans, but at the end of the day, we are expecting investigators and institutions to be accountable to the Plans they have laid out for themselves….
Anticipating that variation in readiness, and in recognition of the cultural change we are trying to seed, there is a two-year implementation period. This time will be spent developing the information, support, and tools that the biomedical enterprise will need to comply with this new policy. NIH has already provided additional supplementary information – on (1) elements of a data management and sharing plan; (2) allowable costs; and (3) selecting a data repository – in concert with the policy release….(More)”
Paper by Stephanie Russo Carroll et al: “Concerns about secondary use of data and limited opportunities for benefit-sharing have focused attention on the tension that Indigenous communities feel between (1) protecting Indigenous rights and interests in Indigenous data (including traditional knowledges) and (2) supporting open data, machine learning, broad data sharing, and big data initiatives. The International Indigenous Data Sovereignty Interest Group (within the Research Data Alliance) is a network of nation-state based Indigenous data sovereignty networks and individuals that developed the ‘CARE Principles for Indigenous Data Governance’ (Collective Benefit, Authority to Control, Responsibility, and Ethics) in consultation with Indigenous Peoples, scholars, non-profit organizations, and governments. The CARE Principles are people– and purpose-oriented, reflecting the crucial role of data in advancing innovation, governance, and self-determination among Indigenous Peoples. The Principles complement the existing data-centric approach represented in the ‘FAIR Guiding Principles for scientific data management and stewardship’ (Findable, Accessible, Interoperable, Reusable). The CARE Principles build upon earlier work by the Te Mana Raraunga Maori Data Sovereignty Network, US Indigenous Data Sovereignty Network, Maiam nayri Wingara Aboriginal and Torres Strait Islander Data Sovereignty Collective, and numerous Indigenous Peoples, nations, and communities. The goal is that stewards and other users of Indigenous data will ‘Be FAIR and CARE.’ In this first formal publication of the CARE Principles, we articulate their rationale, describe their relation to the FAIR Principles, and present examples of their application….(More)” See also Selected Readings on Indigenous Data Sovereignty.
Article by Seth Rosenblatt: “Smartphone location data is a dream for marketers who want to know where you go and how long you spend there—and a privacy nightmare. But this kind of geolocation data could also be used to protect people’s voting rights on Election Day.
The newly founded nonprofit Center for New Data is now tracking voters at the polls using smartphone location data to help researchers understand how easy—or difficult—it is for people to vote in different places. Called the Observing Democracy project, the nonpartisan effort is making data on how far people have to travel to vote and how long they have to wait in line available in a privacy-friendly way so it can be used to craft election policies that ensure voting is accessible for everyone.
Election data has already fueled changes in various municipalities and states. A 66-page lawsuit filed by Fair Fight Action against the state of Georgia in the wake of Stacey Abrams’s narrow loss to Brian Kemp in the 2018 gubernatorial race relies heavily on data to back its assertions of unconstitutionally delayed and deferred voter registration, unfair challenges to absentee and provisional ballots, and unjustified purges of voter rolls—all hallmarks of voter suppression.
The promise of Observing Democracy is to make this type of impactful data available much more rapidly than ever before. Barely a month old, Observing Democracy isn’t wasting any time: Its all-volunteer staffers will be receiving data potentially as soon as Nov. 4 on voter wait times at polling locations, travel times to polling stations, and how frequently ballot drop-off boxes are visited, courtesy of location-data mining companies X-Mode Social and Veraset, which was spun off from SafeGraph….(More)”.
Article by Stephanie Chin and Caitlin Chin: “To improve data sharing during global public health crises, it is time to explore the establishment of a common data space for highly infectious diseases. Common data spaces integrate multiple data sources, enabling a more comprehensive analysis of data based on greater volume, range, and access. At its essence, a common data space is like a public library system, which has collections of different types of resources from books to video games; processes to integrate new resources and to borrow resources from other libraries; a catalog system to organize, sort, and search through resources; a library card system to manage users and authorization; and even curated collections or displays that highlight themes among resources.
Even before the COVID-19 pandemic, there was significant momentum to make critical data more widely accessible. In the United States, Title II of the Foundations for Evidence-Based Policymaking Act of 2018, or the OPEN Government Data Act, requires federal agencies to publish their information online as open data, using standardized, machine-readable data formats. This information is now available on the federal data.gov catalog and includes 50 state- or regional-level data hubs and 47 city- or county-level data hubs. In Europe, the European Commission released a data strategy in February 2020 that calls for common data spaces in nine sectors, including healthcare, shared by EU businesses and governments.
Going further, a common data space could help identify outbreaks and accelerate the development of new treatments by compiling line list incidence data, epidemiological information and models, genome and protein sequencing, testing protocols, results of clinical trials, passive environmental monitoring data, and more.
Moreover, it could foster a common understanding and consensus around the facts—a prerequisite to reach international buy-in on policies to address situations unique to COVID-19 or future pandemics, such as the distribution of medical equipment and PPE, disruption to the tourism industry and global supply chains, social distancing or quarantine, and mass closures of businesses….(More). See also Call for Action for a Data Infrastructure to tackle Pandemics and other Dynamic Threats.
Good Governance Paper by Rebecca Ingber:”…Below I offer four concrete recommendations for deploying Intentional Bureaucratic Architecture within the executive branch. But first, I will establish three key background considerations that provide context for these recommendations. The focus of this piece is primarily executive branch legal decisionmaking, but many of these recommendations apply equally to other areas of policymaking.
First, make room for the views and expertise of career officials. As a political appointee entering a new office, ask those career officials: What are the big issues on the horizon on which we will need to take policy or legal views? What are the problems with the positions I am inheriting? What is and is not working? Where are the points of conflict with our allies abroad or with Congress? Career officials are the institutional memory of the government and often the only real experts in the specific work of their agency. They will know about the skeletons in the closet and where the bodies are buried and all the other metaphors for knowing things that other people do not. Turn to them early. Value them. They will have views informed by experience rather than partisan politics. But all bureaucratic actors, including civil servants, also bring to the table their own biases, and they may overvalue the priorities of their own office over others. Valuing their role does not mean handing the reins over to the civil service—good governance requires exercising judgement and balancing the benefits of experience and expertise with fresh eyes and leadership. A savvy bureaucratic actor might know how to “get around” the bureaucratic roadblocks, but the wise bureaucratic player also knows how much the career bureaucracy has to offer and exercises judgment based in clear values about when to defer and when to overrule.
Second, get ahead of decisions: choose vehicles for action carefully and early. The reality of government life is that much of the big decisionmaking happens in the face of a fire drill. As I’ve written elsewhere, the trigger or “interpretation catalyst” that compels the government to consider and assert a position—in other words, the cause of that fire drill—shapes the whole process of decisionmaking and the resulting decision. When an issue arises in defensive litigation, a litigation-driven process controls. That means that career line attorneys shape the government’s legal posture, drawing from longstanding positions and often using language from old briefs. DOJ calls the shots in a context biased toward zealous defense of past action. That looks very different from a decisionmaking process that results from the president issuing an executive order or presidential memorandum, a White House official deciding to make a speech, the State Department filing a report with a treaty body, or DOD considering whether to engage in an operation involving force. Each of these interpretation catalysts triggers a different process for decisionmaking that will shape the resulting outcome. But because of the stickiness of government decisions—and the urgent need to move on to the next fire drill—these positions become entrenched once taken. That means that the process and outcome are driven by the hazards of external events, unless officials find ways to take the reins and get ahead of them.
And finally, an incoming administration must put real effort into Intentional Bureaucratic Architecture by deliberately and deliberatively creating and managing the bureaucratic processes in which decisionmaking happens. Novel issues arise and fire drills will inevitably happen in even the best prepared administrations. The bureaucratic architecture will dictate how decisionmaking happens from the novel crises to the bread and butter of daily agency work. There are countless varieties of decisionmaking models inside the executive branch, which I have classified in other work. These include a unitary decider model, of which DOJ’s Office of Legal Counsel (OLC) is a prime example, an agency decider model, and a group lawyering model. All of these models will continue to co-exist. Most modern national security decisionmaking engages the interests and operations of multiple agencies. Therefore, in a functional government, most of these decisions will involve group lawyering in some format—from agency lawyers picking up the phone to coordinate with counterparts in other agencies to ad hoc meetings to formal regularized working groups with clear hierarchies all the way up to the cabinet. Often these processes evolve organically, as issues arise. Some are created from the top down by presidential administrations that want to impose order on the process. But all of these group lawyering dynamics often lack a well-defined process for determining the outcome in cases of conflict or deciding how to establish a clear output. This requires rule setting and organizing the process from the top down….(More).
Essay by Jeff Malpas in AI and Society: “In 2016, the Australian Government launched an automated debt recovery system through Centrelink—its Department of Human Services. The system, which came to be known as ‘Robodebt’, matched the tax records of welfare recipients with their declared incomes as held by Ethe Department and then sent out debt notices to recipients demanding payment. The entire system was computerized, and many of those receiving debt notices complained that the demands for repayment they received were false or inaccurate as well as unreasonable—all the more so given that those being targeted were, almost by definition, those in already vulnerable circumstances. The system provoked enormous public outrage, was subjected to successful legal challenge, and after being declared unlawful, the Government paid back all of the payments that had been received, and eventually, after much prompting, issued an apology.
The Robodebt affair is characteristic of a more general tendency to shift to systems of automated decision-making across both the public and the private sector and to do so even when those systems are flawed and known to be so. On the face of it, this shift is driven by the belief that automated systems have the capacity to deliver greater efficiencies and economies—in the Robodebt case, to reduce costs by recouping and reducing social welfare payments. In fact, the shift is characteristic of a particular alliance between digital technology and a certain form of contemporary bureaucratised capitalism. In the case of the automated systems we see in governmental and corporate contexts—and in many large organisations—automation is a result both of the desire on the part of software, IT, and consultancy firms to increase their customer base as well as expand the scope of their products and sales, and of the desire on the part of governments and organisations to increase control at the same time as they reduce their reliance on human judgment and capacity. The fact is, such systems seldom deliver the efficiencies or economies they are assumed to bring, and they also give rise to significant additional costs in terms of their broader impact and consequences, but the imperatives of sales and seemingly increased control (as well as an irrational belief in the benefits of technological solutions) over-ride any other consideration. The turn towards automated systems like Robodebt is, as is now widely recognised, a common feature of contemporary society. To look to a completely different domain, new military technologies are being developed to provide drone weapon systems with the capacity to identify potential threats and defend themselves against them. The development is spawning a whole new field of military ethics-based entirely around the putative ‘right to self-defence’ of automated weapon systems.
In both cases, the drone weapon system and Robodebt, we have instances of the development of automated systems that seem to allow for a form of ‘judgment’ that appears to operate independently of human judgment—hence the emphasis on this systems as autonomous. One might argue—and typically it is so argued—that any flaws that such systems currently present can be overcome either through the provision of more accurate information or through the development of more complex forms of artificial intelligence….(More)”.
CRS Report: “Public health surveillance, or ongoing data collection, is an essential part of public health practice. Particularly during a pandemic, timely data are important to understanding the epidemiology of a disease in order to craft policy and guide response decision making. Many aspects of public health surveillance—such as which data are collected and how—are often governed by law and policy at the state and sub federal level, though informed by programs and expertise at the Centers for Disease Control and Prevention (CDC). The Coronavirus Disease 2019 (COVID-19) pandemic has exposed limitations and challenges with U.S. public health surveillance, including those related to the timeliness, completeness, and accuracy of data.
This report provides an overview of U.S. public health surveillance, current COVID-19 surveillance and data collection, and selected policy issues that have been highlighted by the pandemic.Appendix B includes a compilation of selected COVID-19 data resources….(More)”.
Paper by Lauren Rhue and Anne L. Washington: “Artificial intelligence promises predictions and data analysis to support efficient solutions for emerging problems. Yet, quickly deploying AI comes with a set of risks. Premature artificial intelligence may pass internal tests but has little resilience under normal operating conditions. This Article will argue that regulation of early and emerging artificial intelligence systems must address the management choices that lead to releasing the system into production. First, we present examples of premature systems in the Boeing 737 Max, the 2020 coronavirus pandemic public health response, and autonomous vehicle technology. Second, the analysis highlights relevant management practices found in our examples of premature AI. Our analysis suggests that redundancy is critical to protecting the public interest. Third, we offer three points of context for premature AI to better assess the role of management practices.
AI in the public interest should: 1) include many sensors and signals; 2) emerge from a broad range of sources; and 3) be legible to the last person in the chain. Finally, this Article will close with a series of policy suggestions based on this analysis. As we develop regulation for artificial intelligence, we need to cast a wide net to identify how problems develop within the technologies and through organizational structures….(More)”.