Article by Stefaan Verhulst and Peter Addo: “…At the root of this debate runs a frequent concern with how data is collected, stored, used — and responsibly reused for other purposes that initially collected for…
In this article, we propose that promoting responsible reuse of data requires addressing the power imbalances inherent in the data ecology. These imbalances disempower key stakeholders, thereby undermining trust in data management practices. As we recently argued in a report on “responsible data reuse in developing countries,” prepared for Agence Française de Development (AFD), power imbalences may be particularly pernicious when considering the use of data in the Global South. Addressing these requires broadening notions of consent, beyond current highly individualized approaches, in favor of what we instead term a social license for reuse.
In what follows, we explain what a social license means, and propose three steps to help achieve that goal. We conclude by calling for a new research agenda — one that would stretch existing disciplinary and conceptual boundaries — to reimagine what social licenses might mean, and how they could be operationalized…(More)”.
Paper by Andrew Caplin: “We use a controlled experiment to show that ability and belief calibration jointly determine the benefits of working with Artificial Intelligence (AI). AI improves performance more for people with low baseline ability. However, holding ability constant, AI assistance is more valuable for people who are calibrated, meaning they have accurate beliefs about their own ability. People who know they have low ability gain the most from working with AI. In a counterfactual analysis, we show that eliminating miscalibration would cause AI to reduce performance inequality nearly twice as much as it already does…(More)”.
NOAA Research: “The GReenhouse gas And Air Pollutants Emissions System (GRA²PES), from NOAA and the National Institute of Standards and Technology (NIST), combines information on greenhouse gas and air quality pollutant sources into a single national database, offering innovative interactive map displays and new benefits for both climate and public health solutions.
A new U.S.-based system to combine air quality and greenhouse gas pollution sources into a single national research database is now available in the U.S. Greenhouse Gas Center portal. This geospatial data allows leaders at city, state, and regional scales to more easily identify and take steps to address air quality issues while reducing climate-related hazards for populations.
The dataset is the GReenhouse gas And Air Pollutants Emissions System (GRA²PES). A research project developed by NOAA and NIST, GRA²PES captures monthly greenhouse gas (GHG) emissions activity for multiple economic sectors to improve measurement and modeling for both GHG and air pollutants across the contiguous U.S.
Having the GHG and air quality constituents in the same dataset will be exceedingly helpful, said Columbia University atmospheric scientist Roisin Commane, the lead on a New York City project to improve emissions estimates…(More)”.
Article by Marta Biino: “Google’s DeepMind artificial intelligence research laboratory and German pharma company BioNTech are both building AI-powered lab assistants to help scientists conduct experiments and perform tasks, the Financial Times reported.
It’s the latest example of how developments in artificial intelligence are revolutionizing a number of fields, including medicine. While AI has long been used in radiology, for image analysis, or oncology to classify skin lesions for example, as the technology continues to advance its applications are growing.
OpenAI’s GPT models have outperformed humans in making cancer diagnoses based on MRI reports and beat PhD-holders in standardized science tests, to name a few.
However, as AI’s use in health care expands, some fear the notoriously biased technology could carry negative repercussions for patients…(More)”.
Article by Zhen Yang: “Whether on the web or the app, the home page of The New York Times is a crucial gateway, setting the stage for readers’ experiences and guiding them to the most important news of the day. The Times publishes over 250 stories daily, far more than the 50 to 60 stories that can be featured on the home page at a given time. Traditionally, editors have manually selected and programmed which stories appear, when and where, multiple times daily. This manual process presents challenges:
How can we provide readers a relevant, useful, and fresh experience each time they visit the home page?
How can we make our editorial curation process more efficient and scalable?
How do we maximize the reach of each story and expose more stories to our readers?
To address these challenges, the Times has been actively developing and testing editorially driven algorithms to assist in curating home page content. These algorithms are editorially driven in that a human editor’s judgment or input is incorporated into every aspect of the algorithm — including deciding where on the home page the stories are placed, informing the rankings, and potentially influencing and overriding algorithmic outputs when necessary. From the get-go, we’ve designed algorithmic programming to elevate human curation, not to replace it…
The Times began using algorithms for content recommendations in 2011 but only recently started applying them to home page modules. For years, we only had one algorithmically-powered module, “Smarter Living,” on the home page, and later, “Popular in The Times.” Both were positioned relatively low on the page.
Three years ago, the formation of a cross-functional team — including newsroom editors, product managers, data scientists, data analysts, and engineers — brought the momentum needed to advance our responsible use of algorithms. Today, nearly half of the home page is programmed with assistance from algorithms that help promote news, features, and sub-brand content, such as The Athletic and Wirecutter. Some of these modules, such as the features module located at the top right of the home page on the web version, are in highly visible locations. During major news moments, editors can also deploy algorithmic modules to display additional coverage to complement a main module of stories near the top of the page. (The topmost news package of Figure 1 is an example of this in action.)…(More)”
How is editorial judgment incorporated into algorithmic programming?
Article by Joseph Cox: “A pair of students at Harvard have built what big tech companies refused to release publicly due to the overwhelming risks and danger involved: smart glasses with facial recognition technology that automatically looks up someone’s face and identifies them. The students have gone a step further too. Their customized glasses also pull other information about their subject from around the web, including their home address, phone number, and family members.
The project is designed to raise awareness of what is possible with this technology, and the pair are not releasing their code, AnhPhu Nguyen, one of the creators, told 404 Media. But the experiment, tested in some cases on unsuspecting people in the real world according to a demo video, still shows the razor thin line between a world in which people can move around with relative anonymity, to one where your identity and personal information can be pulled up in an instant by strangers.
Nguyen and co-creator Caine Ardayfio call the project I-XRAY. It uses a pair of Meta’s commercially available Ray Ban smart glasses, and allows a user to “just go from face to name,” Nguyen said…(More)”.
Article by Milad Alucozai: “…A new wave of platforms is emerging to address these limitations. Designed with the modern scientist in mind, these platforms prioritize intuitive interfaces, enabling researchers with diverse computational backgrounds to easily navigate and analyze data. They emphasize collaboration, allowing teams to share data and insights seamlessly. And they increasingly incorporate artificial intelligence, offering powerful tools for accelerating analysis and discovery. This shift marks a move towards more user-centric, efficient, and collaborative computational biology, empowering researchers to tackle increasingly complex biological questions.
Emerging Platforms:
Seqera Labs: Spearheading a movement towards efficient and reproducible research, Seqera Labs provides a suite of tools, including the popular open-source workflow language Nextflow. Their platform empowers researchers to design scalable and reproducible data analysis pipelines, particularly for cloud environments. Seqera streamlines complex computational workflows across diverse biological disciplines by emphasizing automation and flexibility, making data-intensive research scalable, flexible, and collaborative.
Form Bio: Aimed at democratizing access to computational biology, Form Bio provides a comprehensive tech suite built to enable accelerated cell and gene therapy development and computational biology at scale. Its emphasis on collaboration and intuitive design fosters a more inclusive research environment to help organizations streamline therapeutic development and reduce time-to-market.
Code Ocean: Addressing the critical need for reproducibility in research, Code Ocean provides a unique platform for sharing and executing research code, data, and computational environments. By encapsulating these elements in a portable and reproducible format, Code Ocean promotes transparency and facilitates the reuse of research methods, ultimately accelerating scientific discovery.
Pluto Biosciences: Championing a collaborative approach to biological discovery, Pluto Biosciences offers an interactive platform for visualizing and analyzing complex biological data. Its intuitive tools empower researchers to explore data, generate insights, and seamlessly share findings with collaborators. This fosters a more dynamic and interactive research process, facilitating knowledge sharing and accelerating breakthroughs.
Open Source Platform:
Galaxy: A widely used open-source platform for bioinformatics analysis. It provides a user-friendly web interface and a vast collection of tools for various tasks, from sequence analysis to data visualization. Its open-source nature fosters community development and customization, making it a versatile tool for diverse research needs.
Bioconductor is a prominent open-source platform for bioinformatics analysis, akin to Galaxy’s commitment to accessibility and community-driven development. It leverages the power of the R programming language, providing a wealth of packages for tasks ranging from genomic data analysis to statistical modeling. Its open-source nature fosters a collaborative environment where researchers can freely access, utilize, and contribute to a growing collection of tools…(More)”
Book by Paulina Rowińska: “Why are coastlines and borders so difficult to measure? How does a UPS driver deliver hundreds of packages in a single day? And where do elusive serial killers hide? The answers lie in the crucial connection between maps and math.
In Mapmatics, mathematician Paulina Rowińska leads us on a riveting journey around the globe to discover how maps and math are deeply entwined, and always have been. From a sixteenth-century map, an indispensable navigation tool that exaggerates the size of northern countries, to public transport maps that both guide and confound passengers, to congressional maps that can empower or silence whole communities, she reveals how maps and math have shaped not only our sense of space but our worldview. In her hands, we learn how to read maps like a mathematician—to extract richer information and, just as importantly, to question our conclusions by asking what we don’t see…(More)”.
Paper by Amy Whitaker: “While artificial intelligence (AI) stands to transform artistic practice and creative industries, little has been theorized about who owns AI for creative work. Lawsuits brought against AI companies such as OpenAI and Meta under copyright law invite novel reconsideration of the value of creative work. This paper synthesizes across copyright, hybrid practice, and cooperative governance to work toward collective ownership and decision-making. This paper adds to research in arts entrepreneurship because copyright and shared value is so vital to the livelihood of working artists, including writers, filmmakers, and others in the creative industries. Sarah Silverman’s lawsuit against OpenAI is used as the main case study. The conceptual framework of material and machine, one and many, offers a lens onto value creation and shared ownership of AI. The framework includes a reinterpretation of the fourth factor of fair use under U.S. copyright law to refocus on the doctrinal language of value. AI uses the entirety of creative work in a way that is overlooked because of the small scale of one whole work relative to the overall size of the AI model. Yet a theory of value for creative work gives it dignity in its smallness, the way that one vote still has dignity in a national election of millions. As we navigate these frontiers of AI, experimental models pioneered by artists may be instructive far outside the arts…(More)”.
Forum on Democracy and Information: “At a critical time for understanding digital communications’ impact on societies, research on disinformation is endangered.
In August, researchers around the world bid farewell to CrowdTangle – the Meta-owned social media monitoring tool. The decision by Meta to close the number one platform used to track mis- and disinformation, in what is a major election year, only to present its alternative tool Meta Content Library and API, has been met with a barrage of criticism.
If, as suggested by the World Economic Forum’s 2024 global risk report, disinformation is one of the biggest short-term threats to humanity, our collective ability to understand how it spreads and impacts our society is crucial. Just as we would not impede scientific research into the spread of viruses and disease, nor into natural ecosystems or other historical and social sciences, disinformation research must be permitted to be carried out unimpeded and with access to information needed to understand its complexity. Understanding the political economy of disinformation as well as its technological dimensions is also a matter of public health, democratic resilience, and national security.
By directly affecting the research community’s ability to open social media black boxes, this radical decision will also, in turn, hamper public understanding of how technology affects democracy. Public interest scrutiny is also essential for the next era of technology, notably for the world’s largest AI systems, which are similarly proprietary and opaque. The research community is already calling on AI companies to learn from the mistakes of social media and guarantee protections for good faith research. The solution falls on multiple shoulders and the global scientific community, civil society, public institutions and philanthropies must come together to meaningfully foster and protect public interest research on information and democracy…(More)”.
