Paper by Scott D. Kahn and Anne Koralova: “The transition to open data practices is straightforward albeit surprisingly challenging to implement largely due to cultural and policy issues. A general data sharing framework is presented along with two case studies that highlight these challenges and offer practical solutions that can be adjusted depending on the type of data collected, the country in which the study is initiated, and the prevailing research culture. Embracing the constraints imposed by data privacy considerations, especially for biomedical data, must be emphasized for data outside of the United States until data privacy law(s) are established at the Federal and/or State level…(More).”
The Technology Fallacy
Book by Gerald C. Kane, Anh Nguyen Phillips, Jonathan R. Copulsky and Garth R. Andrus on “How People Are the Real Key to Digital Transformation:..
Digital technologies are disrupting organizations of every size and shape, leaving managers scrambling to find a technology fix that will help their organizations compete. This book offers managers and business leaders a guide for surviving digital disruptions—but it is not a book about technology. It is about the organizational changes required to harness the power of technology. The authors argue that digital disruption is primarily about people and that effective digital transformation involves changes to organizational dynamics and how work gets done. A focus only on selecting and implementing the right digital technologies is not likely to lead to success. The best way to respond to digital disruption is by changing the company culture to be more agile, risk tolerant, and experimental.
The authors draw on four years of research, conducted in partnership with MIT Sloan Management Review and Deloitte, surveying more than 16,000 people and conducting interviews with managers at such companies as Walmart, Google, and Salesforce. They introduce the concept of digital maturity—the ability to take advantage of opportunities offered by the new technology—and address the specifics of digital transformation, including cultivating a digital environment, enabling intentional collaboration, and fostering an experimental mindset. Every organization needs to understand its “digital DNA” in order to stop “doing digital” and start “being digital.”
Digital disruption won’t end anytime soon; the average worker will probably experience numerous waves of disruption during the course of a career. The insights offered by The Technology Fallacy will hold true through them all….(More)”.
Smart Streetlights are Casting a Long Shadow Over Our Cities
Article by Zhile Xie: “This is not a surveillance system—nobody is watching it 24 hours a day,” said Erik Caldwell, director of economic development in San Diego, in an interview where he was asked if the wide deployment of “smart” streetlights had turned San Diego into a surveillance city. Innocuous at first glance, this statement demonstrates the pernicious impact of artificial intelligence on new “smart” streetlight systems. As Caldwell suggests, a central human vision is important for the streetlight to function as a surveillance instrument. However, the lack of human supervision only suggests its enhanced capacity. Smart sensors are able to process and communicate environmental information that does not present itself in a visual format and does not rely on human interpretation. On the one hand, they reinforce streetlights’ function as a surveillance instrument, historically associated with light and visibility. On the other hand, in tandem with a wide range of sensors embedded in our everyday environment, they also enable for-profit data extraction on a vast scale, under the auspices of a partnership between local governments and tech corporations.
The streetlight was originally designed as a surveillance device and has been refined to that end ever since then. Its association with surveillance and security can be found as early as 400 BC. Citizens of Ancient Rome started to install an oil lamp in front of every villa to prevent tripping or thefts, and an enslaved person would be designated to watch the lamp—lighting was already paired with the notion of control through slavery. As Wolfgang Schivelbusch has detailed in his book Disenchanted Light, street lighting also emerged in medieval European cities alongside practices of policing. Only designated watchmen who carried a torch and a weapon were allowed to be out on the street. This ancient connection between security and visibility has been the basis of the wide deployment of streetlights in modern cities. Moreover, as Edwin Heathcote has explained in a recent article for the Architectural Review, gas streetlights were first introduced to Paris during Baron Haussmann’s restructuring of the city between 1853 and 1870, which was designed in part to prevent revolutionary uprisings. The invention of electric light bulbs in the late nineteenth century in Europe triggered new fears and imaginations around the use of streetlights for social control. For instance, in his 1894 dystopian novel The Land of the Changing Sun, W.N. Harben envisions an electric-optical device that makes possible 24-hour surveillance over the entire population of an isolated country, Alpha. The telescopic system is aided by an artificial “sun” that lights up the atmosphere all year round, along with networked observatories across the land that capture images of their surroundings, which are transmitted to a “throne room” for inspection by the king and police…(More)”.
Without appropriate metadata, data-sharing mandates are pointless
Article by Mark A. Musen: “Last month, the US government announced that research articles and most underlying data generated with federal funds should be made publicly available without cost, a policy to be implemented by the end of 2025. That’s atop other important moves. The European Union’s programme for science funding, Horizon Europe, already mandates that almost all data be FAIR (that is, findable, accessible, interoperable and reusable). The motivation behind such data-sharing policies is to make data more accessible so others can use them to both verify results and conduct further analyses.
But just getting those data sets online will not bring anticipated benefits: few data sets will really be FAIR, because most will be unfindable. What’s needed are policies and infrastructure to organize metadata.
Imagine having to search for publications on some topic — say, methods for carbon reclamation — but you could use only the article titles (no keywords, abstracts or search terms). That’s essentially the situation for finding data sets. If I wanted to identify all the deposited data related to carbon reclamation, the task would be futile. Current metadata often contain only administrative and organizational information, such as the name of the investigator and the date when the data were acquired.
What’s more, for scientific data to be useful to other researchers, metadata must sensibly and consistently communicate essentials of the experiments — what was measured, and under what conditions. As an investigator who builds technology to assist with data annotation, it’s frustrating that, in the majority of fields, the metadata standards needed to make data FAIR don’t even exist.
Metadata about data sets typically lack experiment-specific descriptors. If present, they’re sparse and idiosyncratic. An investigator searching the Gene Expression Omnibus (GEO), for example, might seek genomic data sets containing information on how a disease or condition manifests itself in young animals or humans. Performing such a search requires knowledge of how the age of individuals is represented — which in the GEO repository, could be age, AGE, age (after birth), age (years), Age (yr-old) or dozens of other possibilities. (Often, such information is missing from data sets altogether.) Because the metadata are so ad hoc, automated searches fail, and investigators waste enormous amounts of time manually sifting through records to locate relevant data sets, with no guarantee that most (or any) can be found…(More)”.
The New ADP National Employment Report
Press Release: “The new ADP National Employment Report (NER) launched today in collaboration with the Stanford Digital Economy Lab. Earlier this spring, the ADP Research Institute paused the NER in order to refine the methodology and design of the report. Part of that evolution was teaming up data scientists at the Stanford Digital Economy Lab to add a new perspective and rigor to the report. The new report uses fine-grained, high-frequency data on jobs and wages to deliver a richer and more useful analysis of the labor market.
Let’s take a look at some of the key changes with the new NER, along with the new ADP® Pay Insights Report.
It’s independent. The key change is that the new ADP NER is an independent measure of the US labor market, rather than a forecast of the BLS monthly jobs number. Jobs report and pay insights are based on anonymized and aggregated payroll data from more than 25 million US employees across 500,000 companies. The new report focuses solely on ADP’s clients and private-sector change…(More)”.
Macroscopes
Exhibit by Places and Spaces: “The term “macroscope” may strike many as being strange or even daunting. But actually, the term becomes friendlier when placed within the context of more familiar “scopes.” For instance, most of us have stared through a microscope. By doing so, we were able to see tiny plant or animal cells floating around before our very eyes. Similarly, many of us have peered out through a telescope into the night sky. There, we were able to see lunar craters, cloud belts on Jupiter, or the phases of Mercury. What both of these scopes have in common is that they allow the viewer to see objects that could otherwise not be perceived by the naked eye, either because they are too small or too distant.
But what if we want to better understand the complex systems or networks within which we operate and which have a profound, if often unperceived, impact on our lives? This is where macroscopes become such useful tools. They allow us to go beyond our focus on the single organism, the single social or natural phenomenon, or the single development in technology. Instead, macroscopes allow us to gather vast amounts of data about many kinds of organisms, environments, and technologies. And from that data, we can analyze and comprehend the way these elements co-exist, compete, or cooperate.
With the macroscope, we are allowed to see the “big picture,” a goal imagined in 1979 by Joël de Rosnay in his groundbreaking book, The Macroscope: A New World Scientific System. For the author, the macroscope would be the “symbol of a new way of seeing and understanding.” It was to be a tool “not used to make things larger or smaller but to observe what is at once too great, too slow, and too complex for our eyes.”
With these needs and insights in mind, the second decade of the Places & Spaces exhibit will invite and showcase interactive visualizations—our own exemplars of de Rosnay’s macroscope—that demonstrate the impact of different data cleaning, analysis, and visualization algorithms. It is the exhibit’s hope that this view of the “behind the scenes” process of data visualization will increase the ability of viewers to gain meaningful insights from such visualizations and empower people from all backgrounds to use data more effectively and endeavor to create maps that address their own needs and interests…(More)”.
Spirals of Delusion: How AI Distorts Decision-Making and Makes Dictators More Dangerous
Essay by Henry Farrell, Abraham Newman, and Jeremy Wallace: “In policy circles, discussions about artificial intelligence invariably pit China against the United States in a race for technological supremacy. If the key resource is data, then China, with its billion-plus citizens and lax protections against state surveillance, seems destined to win. Kai-Fu Lee, a famous computer scientist, has claimed that data is the new oil, and China the new OPEC. If superior technology is what provides the edge, however, then the United States, with its world class university system and talented workforce, still has a chance to come out ahead. For either country, pundits assume that superiority in AI will lead naturally to broader economic and military superiority.
But thinking about AI in terms of a race for dominance misses the more fundamental ways in which AI is transforming global politics. AI will not transform the rivalry between powers so much as it will transform the rivals themselves. The United States is a democracy, whereas China is an authoritarian regime, and machine learning challenges each political system in its own way. The challenges to democracies such as the United States are all too visible. Machine learning may increase polarization—reengineering the online world to promote political division. It will certainly increase disinformation in the future, generating convincing fake speech at scale. The challenges to autocracies are more subtle but possibly more corrosive. Just as machine learning reflects and reinforces the divisions of democracy, it may confound autocracies, creating a false appearance of consensus and concealing underlying societal fissures until it is too late.
Early pioneers of AI, including the political scientist Herbert Simon, realized that AI technology has more in common with markets, bureaucracies, and political institutions than with simple engineering applications. Another pioneer of artificial intelligence, Norbert Wiener, described AI as a “cybernetic” system—one that can respond and adapt to feedback. Neither Simon nor Wiener anticipated how machine learning would dominate AI, but its evolution fits with their way of thinking. Facebook and Google use machine learning as the analytic engine of a self-correcting system, which continually updates its understanding of the data depending on whether its predictions succeed or fail. It is this loop between statistical analysis and feedback from the environment that has made machine learning such a formidable force…(More)”
Belfast to launch ‘Citizen Office of Digital Innovation’
Article by Sarah Wray: The City of Belfast in Northern Ireland has launched a tender to develop and pilot a Citizen Office of Digital Innovation (CODI) – a capacity-building programme to boost resident engagement around data and technology.
The council says the pilot will support a ‘digital citizenship skillset’, enabling citizens to better understand and shape how technology is used in Belfast. It could also lead to the creation of tools that can be used and adapted by other cities under a creative commons licence.
The tender is seeking creative and interactive methods to explore topics such as co-design, citizen science, the Internet of Things, artificial intelligence and data science, and privacy. It cites examples of citizen-centric programmes elsewhere including Dublin’s Academy of the Near Future and the DTPR standard for visual icons to explain sensors and cameras that are deployed in public spaces…(More)”
Participatory Data Governance: How Small Changes Can Lead to Greater Inclusion
Essay by Kate Richards and Martina Barbero: “What the majority of participatory data governance approaches have in common is strong collaboration between public authorities and civil society organizations and representatives of communities that have been historically marginalized and excluded or who are at risk of being marginalized. This leads to better data and evidence for policy-making. For instance, a partnership between the Canadian government and First Nations communities led Statistics Canada to better understand the factors that exacerbate exclusion and capture the lived experiences of these communities.
These practices are pivotal for increasing inclusion and accountability in data beyond the data collection stage. In fact, while inclusion at the data collection phase remains extremely important, participatory data governance approaches can be adopted at any stage of the data lifecycle.
- Before data collection starts: Building relationships with communities at risk of being marginalized helps clarify “what to count” and how to embed the needs and aspirations of vulnerable populations in new data collection approaches. The National Department of Statistics in Colombia’s (DANE) multi-year work with Indigenous communities enabled the statistical office to change their population survey approach, leading to more inclusive data policies.
- After data is collected: Collaborating with civil society organizations enables public authorities to assess how and through which channels data should be shared with target communities. When the government of Buenos Aires wanted to provide information to increase access to sexual and reproductive health services, it worked with civil society to gather feedback and develop a platform that would be useful and accessible to the target population.
- At the stage of data use: Participatory approaches for data inclusion also support greater data use, both by public authorities and by external stakeholders. In Medellin, Colombia, the availability of more granular and more inclusive data on teen pregnancy enabled the government to develop better prevention policies and establish personalized services for girls at risk, resulting in a reduction of teen pregnancies by 30%. In Rosario, Argentina, the government’s partnership with associations representing persons with disabilities led to the development of much more accessible and inclusive public portals, which in turn resulted in better access to services for all citizens…(More)”.
A little good goes an unexpectedly long way: Underestimating the positive impact of kindness on recipients.
Paper by Kumar, A., & Epley, N. : “Performing random acts of kindness increases happiness in both givers and receivers, but we find that givers systematically undervalue their positive impact on recipients. In both field and laboratory settings (Experiments 1a through 2b), those performing an act of kindness reported how positive they expected recipients would feel and recipients reported how they actually felt. From giving away a cup of hot chocolate in a park to giving away a gift in the lab, those performing a random act of kindness consistently underestimated how positive their recipients would feel, thinking their act was of less value than recipients perceived it to be. Givers’ miscalibrated expectations are driven partly by an egocentric bias in evaluations of the act itself (Experiment 3). Whereas recipients’ positive reactions are enhanced by the warmth conveyed in a kind act, givers’ expectations are relatively insensitive to the warmth conveyed in their action. Underestimating the positive impact of a random act of kindness also leads givers to underestimate the behavioral consequences their prosociality will produce in recipients through indirect reciprocity (Experiment 4). We suggest that givers’ miscalibrated expectations matter because they can create a barrier to engaging in prosocial actions more often in everyday life (Experiments 5a and 5b), which may result in people missing out on opportunities to enhance both their own and others’ well-being…(More)”