Opportunities and Challenges in Reusing Public Genomics Data

Curated on Posted on by Stefaan Verhulst

Introduction to Special Issue by Mahmoud Ahmed and Deok Ryong Kim: “Genomics data is accumulating in public repositories at an ever-increasing rate. Large consortia and individual labs continue to probe animal and plant tissue and cell cultures, generating vast amounts of data using established and novel technologies. The human genome project kickstarted the era of systems biology (1, 2). Ambitious projects followed to characterize non-coding regions, variations across species, and between populations (3, 4, 5). The cost reduction allowed individual labs to generate numerous smaller high-throughput datasets (6, 7, 8, 9). As a result, the scientific community should consider strategies to overcome the challenges and maximize the opportunities to use these resources for research and the public good. In this collection, we will elicit opinions and perspectives from researchers in the field on the opportunities and challenges of reusing public genomics data. The articles in this research topic converge on the need for data sharing while acknowledging the challenges that come with it. Two articles defined and highlighted the distinction between data and metadata. The characteristic of each should be considered when designing optimal sharing strategies. One article focuses on the specific issues surrounding the sharing of genomics interval data, and another on balancing the need for protecting pediatric rights and the sharing benefits.

The definition of what counts as data is itself a moving target. As technology advances, data can be produced in more ways and from novel sources. Events of recent years have highlighted this fact. “The pandemic has underscored the urgent need to recognize health data as a global public good with mechanisms to facilitate rapid data sharing and governance,” Schwalbe and colleagues (2020). The challenges facing these mechanisms could be technical, economic, legal, or political. Defining what data is and its type, therefore, is necessary to overcome these barriers because “the mechanisms to facilitate data sharing are often specific to data types.” Unlike genomics data, which has established platforms, sharing clinical data “remains in a nascent phase.” The article by Patrinos and colleagues (2022) considers the strong ethical imperative for protecting pediatric data while acknowledging the need not to overprotections. The authors discuss a model of consent for pediatric research that can balance the need to protect participants and generate health benefits.

Xue et al. (2023) focus on reusing genomic interval data. Identifying and retrieving the relevant data can be difficult, given the state of the repositories and the size of these data. Similarly, integrating interval data in reference genomes can be hard. The author calls for standardized formats for the data and the metadata to facilitate reuse.

Sheffield and colleagues (2023) highlight the distinction between data and metadata. Metadata describes the characteristics of the sample, experiment, and analysis. The nature of this information differs from that of the primary data in size, source, and ways of use. Therefore, an optimal strategy should consider these specific attributes for sharing metadata. Challenges specifics to sharing metadata include the need for standardized terms and formats, making it portable and easier to find.

We go beyond the reuse issue to highlight two other aspects that might increase the utility of available public data in Ahmed et al. (2023). These are curation and integration…(More)”.

The Prediction Society: Algorithms and the Problems of Forecasting the Future

Curated on Posted on by Stefaan Verhulst

Paper by Hideyuki Matsumi and Daniel J. Solove: “Predictions about the future have been made since the earliest days of humankind, but today, we are living in a brave new world of prediction. Today’s predictions are produced by machine learning algorithms that analyze massive quantities of personal data. Increasingly, important decisions about people are being made based on these predictions.

Algorithmic predictions are a type of inference. Many laws struggle to account for inferences, and even when they do, the laws lump all inferences together. But as we argue in this Article, predictions are different from other inferences. Predictions raise several unique problems that current law is ill-suited to address. First, algorithmic predictions create a fossilization problem because they reinforce patterns in past data and can further solidify bias and inequality from the past. Second, algorithmic predictions often raise an unfalsiability problem. Predictions involve an assertion about future events. Until these events happen, predictions remain unverifiable, resulting in an inability for individuals to challenge them as false. Third, algorithmic predictions can involve a preemptive intervention problem, where decisions or interventions render it impossible to determine whether the predictions would have come true. Fourth, algorithmic predictions can lead to a self-fulfilling prophecy problem where they actively shape the future they aim to forecast.

More broadly, the rise of algorithmic predictions raises an overarching concern: Algorithmic predictions not only forecast the future but also have the power to create and control it. The increasing pervasiveness of decisions based on algorithmic predictions is leading to a prediction society where individuals’ ability to author their own future is diminished while the organizations developing and using predictive systems are gaining greater power to shape the future…(More)”

Making Sense of Citizens’ Input through Artificial Intelligence: A Review of Methods for Computational Text Analysis to Support the Evaluation of Contributions in Public Participation

Curated on Posted on by Stefaan Verhulst

Paper by Julia Romberg and Tobias Escher: “Public sector institutions that consult citizens to inform decision-making face the challenge of evaluating the contributions made by citizens. This evaluation has important democratic implications but at the same time, consumes substantial human resources. However, until now the use of artificial intelligence such as computer-supported text analysis has remained an under-studied solution to this problem. We identify three generic tasks in the evaluation process that could benefit from natural language processing (NLP). Based on a systematic literature search in two databases on computational linguistics and digital government, we provide a detailed review of existing methods and their performance. While some promising approaches exist, for instance to group data thematically and to detect arguments and opinions, we show that there remain important challenges before these could offer any reliable support in practice. These include the quality of results, the applicability to non-English language corpuses and making algorithmic models available to practitioners through software. We discuss a number of avenues that future research should pursue that can ultimately lead to solutions for practice. The most promising of these bring in the expertise of human evaluators, for example through active learning approaches or interactive topic modelling…(More)” See also: Where and when AI and CI meet: exploring the intersection of artificial and collective intelligence towards the goal of innovating how we govern.

The Platformization of Public Participation: Considerations for Urban Planners Navigating New Engagement Tools

Curated on Posted on by Stefaan Verhulst

Paper by Pamela Robinson & Peter Johnson: “Professional urban planners have an ethical obligation to work in the public interest. Public input and critique gathered at public meetings and other channels are used to inform planning recommendations to elected officials. Pre-pandemic, the planning profession worked with digital tools, but in-person meetings were the dominant form of public participation. The pandemic imposed a shift to digital channels and tools, with the result that planners’ use of technology risks unitizing public participation. As the use of new platforms for public participation expands, we argue it has the potential to fundamentally change participation, a process we call platformization. We frame this as a subset of the broader emergence of platform urbanism. This chapter evaluates six public participation platforms, identifying how the tools they provide map onto key participation frameworks from Arnstein (1969), Fung (2006), and IAP2 (2018). Through this analysis, we examine how the platformization of public participation poses ethical and scholarly challenges to the work of professional planners…(More)”.

The messy politics of local climate assemblies

Curated on Posted on by Stefaan Verhulst

Paper by Pancho Lewis,  Jacob Ainscough,  Rachel Coxcoon &  Rebecca Willis: “In recent years, many local authorities in the UK have run local climate assemblies (LCAs) such as citizens’ assemblies or juries, with the goal of developing citizen-led solutions to the climate crisis. In this essay, we argue that a ‘convenient fiction’ often underpins the way local authority actors explain the rationale for running LCAs. This convenient fiction runs as follows: LCAs are commissioned as a response to the climate threat, and local decision-makers work through LCA recommendations to implement appropriate policies in their locality. We suggest that this narrative smooths over and presents as linear a process that is in fact messy and political. LCAs emerge as a result of political pressure and bargaining. Once LCAs have run their course, the extent to which their recommendations are implemented is dependent on power dynamics and institutional capacities. We argue that it is important to surface the messiness and political tensions that underpin the origins and aftermath of local climate assemblies. This achieves three things. First, it helps manage expectations about the impact LCAs are likely to have on the policy process. Second, it broadens understandings of how LCAs can contribute to change. Third, it provides a complex model that actors can use to understand how they can help deliver climate action through politics. We conclude that LCAs are important — if as yet unproven — new interventions in local climate politics, when assessed against this more complex picture…(More)”

AI and Global Governance: Modalities, Rationales, Tensions

Curated on Posted on by Stefaan Verhulst

Paper by Michael Veale, Kira Matus and Robert Gorwa: “Artificial intelligence (AI) is a salient but polarizing issue of recent times. Actors around the world are engaged in building a governance regime around it. What exactly the “it” is that is being governed, how, by who, and why—these are all less clear. In this review, we attempt to shine some light on those questions, considering literature on AI, the governance of computing, and regulation and governance more broadly. We take critical stock of the different modalities of the global governance of AI that have been emerging, such as ethical councils, industry governance, contracts and licensing, standards, international agreements, and domestic legislation with extraterritorial impact. Considering these, we examine selected rationales and tensions that underpin them, drawing attention to the interests and ideas driving these different modalities. As these regimes become clearer and more stable, we urge those engaging with or studying the global governance of AI to constantly ask the important question of all global governance regimes: Who benefits?…(More)”.

Model evaluation for extreme risks

Curated on Posted on by Stefaan Verhulst

Paper by Toby Shevlane et al: “Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further progress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through “dangerous capability evaluations”) and the propensity of models to apply their capabilities for harm (through “alignment evaluations”). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security.

Figure 1 | The theory of change for model evaluations for extreme risk. Evaluations for dangerous capabilities and alignment inform risk assessments, and are in turn embedded into important governance processes…(More)”.

Best Practices for Disclosure and Citation When Using Artificial Intelligence Tools

Curated on Posted on by Stefaan Verhulst

Article by Mark Shope: “This article is intended to be a best practices guide for disclosing the use of artificial intelligence tools in legal writing. The article focuses on using artificial intelligence tools that aid in drafting textual material, specifically in law review articles and law school courses. The article’s approach to disclosure and citation is intended to be a starting point for authors, institutions, and academic communities to tailor based on their own established norms and philosophies. Throughout the entire article, the author has used ChatGPT to provide examples of how artificial intelligence tools can be used in writing and how the output of artificial intelligence tools can be expressed in text, including examples of how that use and text should be disclosed and cited. The article will also include policies for professors to use in their classrooms and journals to use in their submission guidelines…(More)”

Why voters who value democracy participate in democratic backsliding

Curated on Posted on by Stefaan Verhulst

Paper by Braley, A., Lenz, G.S., Adjodah, D. et al.: “Around the world, citizens are voting away the democracies they claim to cherish. Here we present evidence that this behaviour is driven in part by the belief that their opponents will undermine democracy first. In an observational study (N = 1,973), we find that US partisans are willing to subvert democratic norms to the extent that they believe opposing partisans are willing to do the same. In experimental studies (N = 2,543, N = 1,848), we revealed to partisans that their opponents are more committed to democratic norms than they think. As a result, the partisans became more committed to upholding democratic norms themselves and less willing to vote for candidates who break these norms. These findings suggest that aspiring autocrats may instigate democratic backsliding by accusing their opponents of subverting democracy and that we can foster democratic stability by informing partisans about the other side’s commitment to democracy…(More)”

Crime, inequality and public health: a survey of emerging trends in urban data science

Curated on Posted on by Stefaan Verhulst

Paper by Massimiliano Luca, Gian Maria Campedelli, Simone Centellegher, Michele Tizzoni, and Bruno Lepri: “Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations’ Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale…(More)”.